Ultrasound matrix inspection

ABSTRACT

A device and method for performing ultrasound scanning of a substantially cylindrical object, the device comprising a cuff adapted to fit around a circumference of the object, a carrier mounted slidably on the cuff and adapted to traverse the circumference of the object, an ultrasound probe mounted on the carrier and positioned to scan the circumference of the object as the carrier traverses the circumference of the object, a carrier motor mounted on the cuff or the carrier and used to drive the movement of the carrier about the circumference of the object, and one or more data connections providing control information for the carrier motor and the ultrasound probe and receiving scanning data from the ultrasound probe.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of and priority to U.S. ProvisionalPatent Application No. 61/539,208 filed 26 Sep. 2011 and U.S.Provisional Patent Application No. 61/546,217 filed 12 Oct. 2011.

The content of the above patent applications are hereby expresslyincorporated by reference into the detailed description hereof.

FIELD OF INVENTION

This invention relates to methods and devices for carrying outultrasound inspection, and for pipe inspections.

BACKGROUND

US App. Pub. 2011/0087444 to Volker (hereinafter the '444 publication)is directed to a “pig” for crawling through the bore of a pipe andperforming ultrasound inspection of the inner pipe surface. Thereference discloses an algorithm for imaging the pipe surface based onbackscatter signals. The '444 publication involves Fermat's principle todetermine sound paths with the shortest travel time. The modelinginvolves first building a grid and determining travel time for eachpoint in the grid. The '444 reference requires scanning a pipe from theinside, where the primary information to be ascertained is 3Dinformation about the inner surface of the pipe. This does not solvethan the problem of accurately modeling the inner surface of a pipeusing a scanning apparatus positioned on the outer surface.

U.S. Pat. No. 7,685,878 to Brandstrom (hereinafter the '878 patent)relates to a device for rotating a pair of ultrasound transducers arounda pipe circumference for pipe weld inspection. It allows the cables andother apparatus extending away from the transducers to remainstationary, extending away in only a single direction. '878 teaches anapparatus which can be mounted on the pipe at the position adjacent theweld and which carries the transducers and rotates those transducersaround the pipe, bearing in mind that effective access to the pipe isgenerally only available from one side of the pipe.

Two transducers are rotated around a circumferential location on acylindrical body for structural testing of the body, carried on amounting and drive apparatus including a magnetic attachment which canbe manually brought up to a pipe from one side only for fixed connectionto the pipe on that side at a position axially spaced from a weld. Acollar shaped support for the pair of transducers is formed of a row ofseparate segments which wrap around the pipe from the one side and isrotated around the axis of the pipe to carry the transducer around thecircumferential weld. The segments carry rollers to roll on the surfaceand are held against the pipe by magnets. The transducers are carried onthe support in fixed angular position to track their position but in amanner which allows slight axial or radial movement relative to thepipe.

U.S. Pat. No. 7,412,890 to Johnson (hereinafter the '890 patent) relatesto a method and apparatus for detecting cracks in pipe welds comprisingflooding a volume adjacent to the outer pipe surface with water, thenusing phased array ultrasound to scan the pipe surface. The apparatushas a rectangular cavity that has its open bottom surface pressedagainst the pipe surface and is flooded with water. The ultrasound arrayis positioned at the top of the cavity. Phased-array data collectionmethods are used.

U.S. Pat. No. 5,515,298 to Bicz (hereinafter the '298 Patent) relates toan apparatus for performing ultrasound scanning of a fingerprint orother object placed on a concave surface. The apparatus projectsultrasound from an array of transducers through an array of pinholes(one per transducer) and against the concave interior of the surface onwhich the fingerprint rests. The transducers then derive characteristicsof the fingerprint from the reflection and scattering of the sphericalwaveform produced by the pinhole. The apparatus appears to depend on theknown structure of the convexo-concave lens structure of the support onwhich the fingerprint rests.

U.S. Pat. No. 6,896,171 to Den Boer et al (hereinafter the '171 Patent)relates to an apparatus for performing EMAT (electromagnetic acoustictransducer) scanning of a freshly-made pipe weld while still hot. Theapparatus may include an array of EMAT transmitter and receiver coilspositioned on a ring structure around the outer surface of the pipe. Nopost-processing algorithm details are disclosed. The apparatus isdescribed as being able to detect the presence of weld defects, andgives some information as to their size, but neither images, preciselocations, nor are any further details of defects discussed in thedescription.

US App. Pub. No. 2009/0158850 to Alleyne et al (hereinafter the '850publication) relates to a method and apparatus for inspecting pipeswherein the pig apparatus is inserted into the bore of the pipe.Ultrasound transducers are pressed against the inner walls of the pipeand use guided waves (e.g. Lamb waves) of ultrasound within the materialof the pipe wall itself to detect defects. Data collection andprocessing appears to be based on a full matrix capture technique fromwhich different wave modes may be extracted, although a phased-arraydata collection technique may also be used.

US App. Pub. No. 2009/0078742 to Pasquali et al. (hereinafter the '742publication) relates to a method and apparatus for inspectingmulti-walled pipes, such as those used for undersea transport of hot orcold fluids. The method involves placing an ultrasound probe against theinner pipe surface and scanning at various intervals as the proberotates around the inner circumference of the pipe wall. The apparatusis a probe positioned at the end of a rotatable arm, which positions theprobe within the pipe and then rotates it about the circumference of theinner wall. The '742 publication also discloses methods of positioningthe probe at various angles relative to the pipe surface. However, itappears to only teach the use of probes that are displaced from the weldin the pipe's axial direction, and are angled forward or backward towardthe location of the pipe weld.

Additional prior art references include U.S. Pat. No. 7,762,136 to Ume,Ifeanyi C. et al., which teaches ultrasound systems and methods formeasuring weld penetration depth in real time and off line, U.S. Pat.No. 7,694,569 to McGrath, Matthew et al. which teaches a phased arrayultrasonic water wedge apparatus, U.S. Pat. No. 7,694,564 to Brignac,Jacques L. et al. which teaches a boiler tube inspection probe withcentering mechanism and method of operating the same, U.S. Pat. No.6,935,178 to Prause, Reinhard which teaches a device for inspectingpipes using ultrasound, U.S. Pat. No. 6,734,604 to Butler, John V. etal. which teaches a multimode synthesized beam transduction apparatus,U.S. Pat. No. 4,872,130 to Pagano, Dominick A., which teaches anautomated in-line pipe inspection system JP 2004028937 to Furukawa, T.et al., which teaches a method for measuring the shape of a welded pipe.

SUMMARY OF THE INVENTION

Example embodiments described in this document relate to methods anddevices for performing ultrasound inspection of objects using fullmatrix data capture techniques. In a first aspect, the application isdirected to a device for performing ultrasound scanning of a conduit,comprising a cuff adapted to fit around a circumference of the conduit,a carrier mounted slidably on the cuff and adapted to traverse thecircumference of the conduit, an ultrasound probe mounted on the carrierand positioned to scan the circumference of the conduit as the carriertraverses the circumference of the conduit, a carrier motor mounted onthe cuff or the carrier and used to drive the movement of the carrierabout the circumference of the object, and one or more data connectionsproviding control information for the carrier motor and the ultrasoundprobe and receiving scanning data from the ultrasound probe.

In another aspect, the cuff forms a liquid-resistant seal around thecircumference of the conduit, and the device further comprises a liquidfeed for receiving a liquid scanning medium and filling the volumedefined between the interior of the cuff and the exterior of the conduitwith the liquid scanning medium.

In a further aspect, the device further comprises a power connection forreceiving electrical power for the carrier motor.

In a further aspect, the cuff is configurable between an openconfiguration allowing it to be fitted around the conduit and a closedconfiguration encircling the conduit.

In a further aspect, the device further comprises an adjustablereflector mounted to the carrier and a reflector motor for controllingan angle of the adjustable reflector in a plane substantially normal toa longitudinal axis of the object, wherein the ultrasound probe ispositioned to scan the object via reflection of ultrasound signals offof the adjustable reflector, and the one or more data connectionsprovide control information for the reflector motor.

In a further aspect, the device further comprises a power connection forreceiving electrical power for the reflector motor.

In a further aspect, the conduit is a cylinder.

In a further aspect, the ultrasound probe is an array of ultrasoundtransceivers.

In a further aspect, the cuff comprises a knuckle which releasablysecures a first half of said cuff to a second half of said cuff.

In a further aspect, the cuff comprises a first half of said cuffdetachable from a second half of said cuff.

In a further aspect, the application is directed to a method forperforming ultrasound scanning of a conduit, comprising providing anultrasound array having a plurality of ultrasound elements arrayedsubstantially parallel to a longitudinal axis of the conduit,positioning the ultrasound array to project ultrasound signals toward anexternal surface of the object at a first point about the circumferenceof the conduit, performing a full-matrix-capture scan of the first pointabout the circumference of the conduit, comprising: transmitting anultrasound signal from a first ultrasound element in the ultrasoundarray; sensing and recording ultrasound signals received by each otherultrasound element in the ultrasound array; and repeating the steps oftransmitting, sensing and recording, wherein the step of transmitting isperformed in turn by each ultrasound element in the ultrasound arrayother than the first ultrasound element; repositioning the ultrasoundarray at a second point about the circumference of the conduit,performing a full-matrix-capture scan of the second point about thecircumference of the conduit, and repeating the steps of repositioningand performing a full-matrix-capture scan.

In a further aspect, the method further comprises, before performingeach full-matrix-capture scan, transmitting at least one ultrasoundsignal from at least one ultrasound element in the ultrasound array,sensing at least one ultrasound signal received by at least oneultrasound element in the ultrasound array, evaluating at a processorthe quality of the at least one sensed signal, and adjusting a scanningangle of the ultrasound array based on the outcome of the evaluation.

In a further aspect, the ultrasound array projects ultrasound signalstoward the external surface of the object by reflecting the ultrasoundsignals off of an adjustable reflector, and adjusting the scanning angleof the ultrasound array comprises adjusting the angle of the adjustablereflector.

In a further aspect, the application is directed to a method of modelingthe near and far surfaces of an object within a scanning plane passingthrough the near and far surfaces of the object, comprising providing aset of full-matrix-capture ultrasound scanning data corresponding to ascanning area within the scanning plane, the full-matrix-captureultrasound scanning data captured using an ultrasound array transmittingand sensing ultrasound signals through a scanning medium situatedbetween the ultrasound array and the near surface of the object andperforming the steps of: transmitting an ultrasound signal from a firstultrasound element in the ultrasound array; sensing and recordingultrasound signals received by each other ultrasound element in theultrasound array; and repeating the steps of transmitting, sensing andrecording, wherein the step of transmitting is performed by eachultrasound element in the ultrasound array other than the firstultrasound element; constructing a first intensity map of the scanningarea, comprising a plurality of points within the scanning area havingassociated intensity values, by calculating travel times of ultrasoundsignals through the scanning medium based on the full-matrix-captureultrasound scanning data; filtering the first intensity map to model theboundary of the near surface within the scanning area; using the modeledboundary of the near surface as a lens in constructing a secondintensity map, comprising a plurality of points within the scanning areahaving associated intensity values, by the application of Fermat'sPrinciple, to compute ultrasound signal travel times through both thescanning medium and the object based on the full-matrix-captureultrasound scanning data; and filtering the second intensity map tomodel the boundary of the far surface within the scanning area.

In a further aspect, constructing a first intensity map of the scanningarea comprises calculating an intensity I at a plurality of points rwithin the scanning area where I is defined as the sum of the amplitudeof the data-set of analytic time-domain signals from ultrasound arraytransmitter element i to ultrasound array receiver element j at time tfor all i and j, where t is defined for each i,j pair as being the timeit takes for sound to travel through the scanning medium.

In a further aspect, constructing a first intensity map of the scanningarea comprises calculating an intensity I at a plurality of points rwithin the scanning area defined by the equation I

${I\left( {r,a} \right)} = {{{{{\sum_{i,{j \in a}}{I\left( {r,a} \right)}} =}}{\sum_{i,{j \in a}}{I\left( {r,a} \right)}}} = {{\sum_{-}{\left( {i,{j \in a}} \right)\left( {{{g_{-}(i)}{j\left( {t = \left( {{{{e_{-}\left( (i) \right)} - r}} + {{{{e_{-}j} - r}}/c}} \right)} \right)}{{g_{{(i)}j}\left( {t = \frac{{{e_{(i)} - r}} + {{e_{j} - r}}}{c}} \right)}}{I\left( {r,a} \right)}} = {{\sum_{-}{\left( {i,{j \in a}} \right)\left( {{g_{-}(i)}{j\left( {t = {{\left( {{{{e_{-}\left( (i) \right)} - r}} + {{{{e_{-}j} - r}}/c}} \right){{g_{{(i)}j}\left( {t = \frac{{{e_{(i)} - r}} + {{e_{j} - r}}}{c}} \right)}}\; {I(r)}} = {{\sum_{i,j}{g_{{(i)}j}\left( {t = \frac{{{e_{(i)} - r}} + {{e_{j} - r}}}{c}} \right)}}}}} \right.}} \right.}}}} \right.}}}}$

wherein g_((i)j)(t) is the amplitude of the data-set of analytictime-domain signals from ultrasound array transmitter element i toultrasound array receiver element j at time t, r is the vector definingpoint r relative to a coordinate origin, e_((i)) is a vector definingthe position of ultrasound array transmitter element i relative to thecoordinate origin, e_(j) is a vector defining the position of ultrasoundarray receiver element j relative to the coordinate origin, and c is thespeed of sound traveling through the scanning medium.

In a further aspect, constructing a first intensity map of the scanningarea comprises calculating an intensity I at a plurality of points rwithin the scanning area, each point intensity being calculated at aplurality of apertures defined by a fixed plurality of ultrasound arrayelements and the highest intensity of point r calculated for a singleaperture being used to represent the intensity of point r in theintensity map.

In a further aspect, constructing a first intensity map of the scanningarea comprises calculating an intensity I at a plurality of points rwithin the scanning area defined by the equation I

${I\left( {r,a} \right)} = {{{{{\sum_{i,{j \in a}}{I\left( {r,a} \right)}} =}}{\sum_{i,{j \in a}}{I\left( {r,a} \right)}}} = {{\sum_{-}{\left( {i,{j \in a}} \right)\left( {{{g_{-}(i)}{j\left( {t = \left( {{{{e_{-}\left( (i) \right)} - r}} + {{{{e_{-}j} - r}}/c}} \right)} \right)}{{g_{{(i)}j}\left( {t = \frac{{{e_{(i)} - r}} + {{e_{j} - r}}}{c}} \right)}}{I\left( {r,a} \right)}} = {{\sum_{-}{\left( {i,{j \in a}} \right)\left( {{g_{-}(i)}{j\left( {t = {{\left( {{{{e_{-}\left( (i) \right)} - r}} + {{{{e_{-}j} - r}}/c}} \right){{g_{{(i)}j}\left( {t = \frac{{{e_{(i)} - r}} + {{e_{j} - r}}}{c}} \right)}}\; \mspace{20mu} {I(r)}} = {{\max\limits_{a \in A}{\left\{ {I\left( {r,a} \right)} \right\} {wherein}\mspace{14mu} {I\left( {r,a} \right)}}} = {{\sum_{i,j}{g_{{(i)}j}\left( {t = \frac{{{e_{(i)} - r}} + {{e_{j} - r}}}{c}} \right)}}}}}} \right.}} \right.}}}} \right.}}}}$

and wherein g_((i)j)(t) is the amplitude of the data-set of analytictime-domain signals from ultrasound array transmitter element i toultrasound array receiver element j at time t, r is the vector definingpoint r relative to a coordinate origin, e_((i)) is a vector definingthe position of ultrasound array transmitter element i relative to thecoordinate origin, e_(j) is a vector defining the position of ultrasoundarray receiver element j relative to the coordinate origin, c is thespeed of sound traveling through the scanning medium, a is an aperturedefined by a fixed plurality of adjacent ultrasound elements in theultrasound array, and A is a set comprising a plurality of suchapertures.

In a further aspect, using the modeled boundary of the near surface as alens in constructing a second intensity map comprises calculating anintensity I at a plurality of points r within the scanning area definedby the equation I

${I\left( {r,a} \right)} = {{{{{\sum_{i,{j \in a}}{I\left( {r,a} \right)}} =}}{\sum_{i,{j \in a}}{I\left( {r,a} \right)}}} = {{\sum_{-}{\left( {i,{j \in a}} \right)\left( {{{g_{-}(i)}{j\left( {t = \left( {{{{e_{-}\left( (i) \right)} - r}} + {{{{e_{-}j} - r}}/c}} \right)} \right)}{{g_{{(i)}j}\left( {t = \frac{{{e_{(i)} - r}} + {{e_{j} - r}}}{c}} \right)}}{I\left( {r,a} \right)}} = {{\sum_{-}{\left( {i,{j \in a}} \right)\left( {{g_{-}(i)}{j\left( {t = {{\left( {{{{e_{-}\left( (i) \right)} - r}} + {{{{e_{-}j} - r}}/c}} \right){{g_{{(i)}j}\left( {t = \frac{{{e_{(i)} - r}} + {{e_{j} - r}}}{c}} \right)}}\; \mspace{20mu} {I(r)}} = {{\max\limits_{a \in A}{\left\{ {I\left( {r,a} \right)} \right\} \mspace{20mu} {wherein}\mspace{14mu} \mspace{20mu} {I\left( {r,a} \right)}}} = {{\sum\limits_{i,{j \in a}}^{\;}{\sum\limits_{t^{\prime} \in {T_{ij}^{\kappa}{(r)}}}{g_{{(i)}j}\left( t^{\prime} \right)}}}}}}} \right.}} \right.}}}} \right.}}}}$

and wherein

T _(ir) ^(K)(r)={t _(ir) +t _(ir) |t _(ir) εT _(ir) ^(K) ,t _(ir) εT_(jr) ^(K)}

and wherein T_(ir) ^(K) is the set of all first-order and multiple-orderderivatives of the time it takes sound to travel from ultrasound arraytransmitter element i to a point K on the boundary of the near surface,T_(jr) ^(K) is the set of all first-order and multiple-order derivativesof the time it takes sound to travel from ultrasound array receiverelement j to a point K on the boundary of the near surface, g_((i)j)(t)is the amplitude of the data-set of analytic time-domain signals fromultrasound array transmitter element i to ultrasound array receiverelement j at time t, r is the vector defining point r relative to acoordinate origin, e_((i)) is a vector defining the position ofultrasound array transmitter element i relative to the coordinateorigin, e_(j) is a vector defining the position of ultrasound arrayreceiver element j relative to the coordinate origin, c is the speed ofsound traveling through the scanning medium, a is an aperture defined bya fixed plurality of adjacent ultrasound elements in the ultrasoundarray, and A is a set comprising a plurality of such apertures.

In a further aspect, the method further comprises, before constructing afirst intensity map, filtering the full-matrix-capture ultrasoundscanning data to remove noise.

In a further aspect, filtering the first intensity map comprises passingthe intensity map through an edge-detection filter and using the outputas a model of the boundary of the near surface within the scanning area,and filtering the second intensity map comprises passing the intensitymap through an edge-detection filter and using the output as a model ofthe boundary of the far surface within the scanning area.

In a further aspect, filtering the first intensity map and filtering thesecond intensity map each further comprise dilation of the detectededges produced by the edge-detection filter.

In a further aspect, filtering the first intensity map and filtering thesecond intensity map each further comprise thinning the dilated edges.

In a further aspect, filtering the first intensity map and filtering thesecond intensity map each further comprise selecting a single componentfrom each vertical slice of the intensity map and removing all othercomponents in that slice in order to maximize the continuity and lengthof the remaining components.

In a further aspect, the application is directed to a method of modelingthe near and far surfaces of an object, comprising applying the methodsabove to a plurality of sets of full-matrix-capture ultrasound scanningdata corresponding to a plurality of scanning planes passing through thenear and far surfaces of the object, and modeling the near and farsurfaces of the object based on the modeled boundaries within eachscanning plane and the relative locations of each scanning plane.

In a further aspect, the plurality of scanning planes are parallel toand adjacent to each other.

In a further aspect, the object is substantially cylindrical, and theplurality of scanning planes all pass through the longitudinal axis ofthe object.

In a further aspect, the application is directed to a device forperforming ultrasound scanning of an object, comprising a body adaptedto fit on the object; an ultrasound probe mounted on the body andpositioned to scan the body; one or more data connections providingcontrol information for the carrier motor and the ultrasound probe andreceiving scanning data from the ultrasound probe; an adjustablereflector mounted to the carrier; and a reflector motor for controllingan angle of the adjustable reflector in a plane substantially normal toa longitudinal axis of the object, wherein: the ultrasound probe ispositioned to scan the object via reflection of ultrasound signals offof the adjustable reflector; and the one or more data connectionsprovide control information for the reflector motor.

In a further aspect, the body forms a liquid-resistant seal around thecircumference of the object, and the device further comprises a liquidfeed for receiving a liquid scanning medium and filling the volumedefined between the interior of the body and the exterior of the objectwith the liquid scanning medium.

In a further aspect, the device further comprises a power connection forreceiving electrical power for the carrier motor.

In a further aspect, the device further comprises a power connection forreceiving electrical power for the reflector motor.

In a further aspect, the ultrasound probe is an array of ultrasoundtransceivers.

Other example embodiments of the present disclosure will be apparent tothose of ordinary skill in the art from a review of the followingdetailed description in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of an ultrasound probe manipulatoraccording to an example embodiment, operationally fitted to a pipe;

FIG. 2 is a perspective view of the example probe manipulator of FIG. 1;

FIG. 3 is a side view of the example probe manipulator of FIG. 1operationally fitted to a pipe;

FIG. 4 is an isometric view of the example probe manipulator of FIG. 1operationally fitted to a pipe;

FIG. 5 is a perspective view of a hinged probe manipulator according toan example embodiment, showing the manipulator in an open configuration;

FIG. 6 is an isometric view of a hinged probe manipulator according toan example embodiment, showing the manipulator disassembled into twohalves and a hinged knuckle;

FIG. 7 is a side view of a linear multi-element ultrasound probe array;

FIG. 8 is a diagram of an N by N matrix of ultrasound scan datagenerated by the Full Matrix Capture technique using an N-elementultrasound probe array;

FIG. 9 is an isometric representation of an N by N by M matrix of A-scandata as in FIG. 8, showing the third dimension of M samples recordedover time for each A-scan;

FIG. 10 is a side view of a linear ultrasound probe array scanning avolume through an intermediate medium;

FIG. 11 is a graph of the curve T_(ir)(K) from point u₁ to u₂ on thesurface of the object being scanned;

FIG. 12 is an intensity map in the scanning plane of the outer surfaceof a pipe wall;

FIG. 13 is an intensity map in the scanning plane of the inner surfaceof a pipe wall;

FIG. 14 is a graph of the outer surface and inner surface of a pipe wallderived from intensity maps in the scanning plane;

FIG. 15 is a graph of an the real part and the envelope of an A-scansignal before wave packet normalization;

FIG. 16 is a graph of an the real part and the envelope of an A-scansignal after wave packet normalization;

FIG. 17 is a flow chart showing the steps carried out in modeling ofouter and inner surfaces of a scanned object according to an exampleembodiment;

FIG. 18 is a flow chart showing the steps involved in pre-processing ofcaptured ultrasound data according to an example embodiment;

FIG. 19 is a cross-sectional view of an X-Z scanning plane through apipe wall showing how an intensity map is built in an exampleembodiment;

FIG. 20 is a flow chart showing the steps involved in building anintensity map using the Shifting Aperture Focus Method according to anexample embodiment;

FIG. 21 is a flow chart showing the steps involved in boundaryrecognition of an intensity map according to an example embodiment;

FIG. 22 is an example black-and-white image used in conjunction withFIGS. 23 through 25 to illustrate the effect of erosion and dilationoperations;

FIG. 23 is an example 5×5 matrix, centered around the origin, used asthe structuring element in conjunction with FIGS. 22, 24 and 25 toillustrate the effect of erosion and dilation operations;

FIG. 24 is a black-and-white image showing the effect of an erosionoperation on FIG. 22 using the structuring element of FIG. 23;

FIG. 25 is a black-and-white image showing the effect of a dilationoperation on FIG. 22 using the structuring element of FIG. 23;

FIG. 26( a) is an example black-and-white image used in conjunction withFIG. 26( b) to illustrate the effect of canny edge detection;

FIG. 26( b) is a black-and-white image showing the effect of canny edgedetection on FIG. 26( a);

FIG. 27( a) is an example black-and-white image used in conjunction withFIG. 27( b) to illustrate the effect of a thinning algorithm;

FIG. 27( b) is a black-and-white image showing the effect of a thinningalgorithm on FIG. 27( a);

FIG. 28( a) is an example black-and-white image showing a junction;

FIG. 28( b) is an enlarged view of the junction of FIG. 28( a), showingthe rectangular path traced around the junction to count light-darkcycles for junction detection;

FIG. 29 is a graph of an example A-scan time-domain signal showing thetravel time from transmitter excitation to the leading edge of areceived wave packet;

FIG. 30 is an example intensity map of a scanned area;

FIG. 31 is a plot of the edges of the intensity map FIG. 30 plotting thepixels of maximum intensity in the vertical direction;

FIG. 32 is a plot of boundary edges near and above the high intensitypixels of FIG. 31;

FIG. 33 is a plot of an example output of a boundary detection algorithmwithout error correction;

FIG. 34 is a plot of the boundary of FIG. 33 after a dilation operationis performed with a rectangular structuring element;

FIG. 35 is a plot of the boundary of FIG. 34 with a thinning algorithmapplied to it;

FIG. 36 is a plot of the boundary of FIG. 35 with erroneous pixelsremoved;

FIG. 37 is an intensity map image of an inner pipe surface (ID);

FIG. 38 is the intensity map of FIG. 37 after applying edge detection,dilation and thinning algorithms, with a junction area of the boundarycircled;

FIG. 39 is an enlarged view of the circled junction area of FIG. 38;

FIG. 40 is the enlarged view of FIG. 39 with the junction removed;

FIG. 41 is the enlarged view of FIG. 40 with bottom portions ofconnected components removed;

FIG. 42 is the enlarged view of FIG. 41 with small connected componentsremoved;

FIG. 43 is a pipe surface intensity map showing the edge output from aCanny edge detector and a graph of the maximum intensity pixelsapproximating the shape of the true boundary as a flat plate;

FIG. 44 is an pipe surface intensity map showing interpolation of theboundary with areas between two boundary segments extracted from theedge detection process connected with straight lines;

FIG. 45 is a flow chart of the sequential operation of an examplealgorithm for boundary recognition and definition;

FIG. 46 is a side view of an ultrasound probe carrier showing anadjustable reflector, for use with the example manipulator of FIGS. 1 to6;

FIG. 47 is a side view of the ultrasound probe carrier of FIG. 46, shownfrom the opposite side;

FIG. 48 is a cross-sectional side view of a pipe wall showing uneventhinning;

FIG. 49 is a cross-sectional side view of a pipe wall at a fitting tofitting weld showing a zone inspected for thickness;

FIG. 50 is a cross-sectional side view of a pipe wall at a Grayloc™fitting to fitting weld showing a zone inspected for thickness;

FIG. 51 is a network diagram showing remote data acquisition andanalysis computers according to an example embodiment;

FIG. 52 is a network diagram showing the relation of local hardwarecomponents used at the inspection site according to an exampleembodiment;

FIG. 53 is a diagram of the equipment configuration at the remoteacquisition site according to an example embodiment;

FIG. 54 is an example Distance Amplitude Curve (DAC);

FIG. 55 is a 2″ reference block specimen used for calibrating a 2″manipulator according to an example embodiment;

FIG. 56 is a 2″ reference block specimen used for calibrating a 2″manipulator in a 6 degree configuration according to an exampleembodiment;

FIG. 57 is a 2.5″ reference block specimen used for calibrating a 2.5″manipulator according to an example embodiment;

FIG. 58 is a perspective view of a calibration block setup showing anexample manipulator in an open configuration prior to calibration;

FIG. 59 is a flow chart showing the steps performed during calibrationof an example manipulator using an example calibration block accordingto an example embodiment;

FIG. 60 is a flow chart showing the steps performed during inspection ofa pipe diameter according to an example embodiment;

FIG. 61 is a flow chart of steps performed by a data analyst in theprocess of inspection data analysis according to an example embodiment;

FIG. 62 is a flow chart of steps performed by a data analyst and thevarious steps applied by the analysis algorithms on the gateway server,blade server, and local analysis PC in the process of inspection dataanalysis according to an example embodiment;

FIG. 63 is an example Main Results window in the NEOVISION™ applicationused by a data analyst in some embodiments;

FIG. 64 is an example 3D window in the NEOVISION™ application used by adata analyst in some embodiments;

FIG. 65 is an example 3D Overview window in the NEOVISION™ applicationused by a data analyst in some embodiments;

FIG. 66 is an example 3D Pop-up window in the NEOVISION™ applicationused by a data analyst in some embodiments;

FIG. 67 is an example data flow for IFM OD boundary preparation;

FIG. 68 is a detail view of the samples of interest in a time-domainultrasound signal showing the time period during which data samples areto be retained according to an example embodiment;

FIG. 69 is a flow chart showing the entire boundary recognition dataflow according to an example embodiment;

FIG. 70 is an example data flow for boundary definition;

FIG. 71 is a data flow chart for the Interior Focusing Method process;

FIG. 72 is a side cross-sectional view through an example pipe weldshowing example definitions of thickness;

FIG. 73 is a diagram showing the relationship of Cartesian and sphericalcoordinate systems mapped onto an example linear ultrasound array havinglength L;

FIG. 74 is a graph of ultrasound beam directivity showing amplitudeincreasing from 0 to 1 on the vertical axis and angle from −90 degreesto 90 degrees on the horizontal axis for an example embodiment havinga=0.23 mm, f=7.5 MHz, and c=1480 m/s;

FIG. 75 is a spectral graph of element directivity showing angledecreasing from 90 to −90 on the vertical axis and ultrasound transducerelement size increasing from 0 mm to 0.3 mm on the horizontal axis, andhaving a legend for colour values on the right side of the figureranging from amplitude 0 to amplitude 1;

FIG. 76( a) is a graph of array directivity where the steering angleθ_(s) equals 0 degrees, showing angle θ decreasing along the verticalaxis from 90 degrees to −90 degrees and element size increasing from 0mm to 0.28 mm along the horizontal axis, and having a legend for colourvalues on the right side of the figure ranging from amplitude 0 toamplitude 1;

FIG. 76( b) is a graph of array directivity where the steering angleθ_(s) equals 30 degrees, showing angle θ decreasing along the verticalaxis from 90 degrees to −90 degrees and element size increasing from 0mm to 0.28 mm along the horizontal axis, and having a legend for colourvalues on the right side of the figure ranging from amplitude 0 toamplitude 1;

FIG. 77( a) is the pressure field from a 0.25 mm width, 5 mm elevationtransducer radiating a 7.5 MHz continuous sine wave intersecting onexample planar space P₁, showing Z-axis distance increasing from 0 m to0.015 m along the vertical axis and Y-axis distance increasing from 0 mto 0.005 m along the vertical axis, and having a legend for colourvalues on the right side of the figure ranging from intensity −0.06 tointensity 0.06;

FIG. 77( b) is the pressure field from a 0.25 mm width, 5 mm elevationtransducer radiating a 7.5 MHz continuous sine wave intersecting onexample planar space P₂, showing Z-axis distance increasing from 0 m to0.015 m along the vertical axis and Y-axis distance increasing from 0 mto 0.005 m along the vertical axis, and having a legend for colourvalues on the right side of the figure ranging from intensity −0.06 tointensity 0.06;

FIG. 78 is an example intensity map showing the areas of the intensitymap used to generate the Actual Quality Index;

FIG. 79 is a side view of an example probe manipulator having atemperature sensor; and

FIG. 80 is an isometric partial view of the example probe manipulator ofFIG. 79.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

Example embodiments of the invention relate to ultrasound imagingdevices and methods for capture and post-processing of ultrasoundinspection data. In particular, the described example embodiments relateto devices and methods for inspecting pipe welds using a mechanical cuffthat fits around a pipe in the weld region and rotates an ultrasoundtransceiver array around the circumference of the pipe as the arrayperforms multiple transmit-receive cycles of the pipe volume via theFull Matrix Capture data acquisition technique. All data from thetransmit-receive cycles is retained. The data is then post-processedusing a two-step algorithm. First, the outer surface of the pipe ismodeled by constructing an intensity map of the surface and filteringthis map to detect the boundary of the outer surface. Second, the modelof the outer surface constructed during the first step is used as a lensin modeling the inner surface of the pipe, using Fermat's principle. Theinner surface is modeled the same way as the outer surface: an intensitymap is built, then filtered to detect the boundary.

The mechanical cuff has a cylindrical outer structure having watertightseals on either end for sealing against a pipe surface. It receives astream of water via a tube and fills the volume between the structureand the pipe surface with water while in operation in order tofacilitate ultrasound scanning.

The cuff also has an inner rotating ring having on its inner surface alinear array of ultrasound transceiver crystals with the longitudinalaxis of the array aligned along the length of the cylindrical structure,normal to the rotational direction of the inner ring around thecircumference of the pipe. The inner ring is automatically rotatedaround the pipe surface in operation while the outer structure of thecuff remains stationary.

Data is acquired by rotating the inner ring around the circumference ofthe pipe while performing multiple transmit-receive cycles with theultrasound array for each frame. Each frame uses the Full Matrix Capturetechnique: a single element is pulsed, with each element in the arraymeasuring the response at that position and storing the resultingtime-domain signal (A-scan). This process is then repeated, pulsing eachelement in turn and recording the response at each element, resulting ina total data corpus of (N×N) A-scans for an array having N elements. Inthis invention, the stored time period of each A-scan is determined bymonitoring for a signal spike past a set threshold (at time t), thenretroactively recording all signal data beginning at a set intervalbefore the spike (at time t−C).

In situations where the cuff isn't perfectly normal to the pipe surfaceat all points around the circumference, it may be preferable to vary theangle of the array to the pipe surface. For this purpose, the inner ringstructure incorporates an adjustable reflector or mirror for reflectingultrasound waves between the transceiver array and the pipe surface atvarying angles. The mirror may be adjusted automatically by a local orremote processor or controller module that receives probe data andautomatically optimizes the signal quality by adjusting the mirrorangle.

The post-processing algorithm may feature a number of refinements overthe broad outline set out above. Multiple wave modes may be used toimprove the reach and resolution of each probe. The outer surface may bemodeled as multiple surfaces to further improve resolution of the innersurface where the outer surface is highly irregular. In addition, datafrom multiple adjacent “slices” of the pipe or other volume may becombined and overlaid to improve the continuity of the surface model, ordata from two slices of the same area taken at different times may beoverlaid to detect changes in the surfaces over time.

While the invention has been described as a pipe inspection tool andtechnique, the general principles and algorithms are applicable toultrasound imaging in a number of different contexts and applications.

Ultrasound Probe Manipulator Device

With reference to the drawings, FIG. 1 shows an example embodimentcomprising an ultrasound probe manipulator 100. The manipulator 100comprises a cuff 106 that is fitted around the circumference of a pipe 2during the scanning process. The center of the cuff 106 is aligned withthe longitudinal axis 4 of the pipe 2. The manipulator 100 uses a lineararray of ultrasound probe elements, mounted on a carrier (not shown)that traverses the circumference of the cuff 106 by means of a motor128, to scan the slice of pipe encompassed by the cuff 106.

In operation, the cuff 106 is fitted around the pipe 2, with awatertight seal 104 extending from the cuff 106 to the pipe surface. Theinterior volume defined by the inner surface of the cuff 106, the seal104, and the outer pipe surface is then filled with water or anotherfluid suitable for service as an ultrasound scanning medium. In someembodiments, the water is pumped into the interior volume by a hose 110(shown in FIG. 6) incorporated into the manipulator 100. The hose 110 isconnected to an external water source and/or pump, and feeds into theinterior volume of the cuff 106 via a hose intake 132 (shown in FIG. 2).

One or more data connections connect the manipulator 100 to one or moreexternal data processing systems and/or controllers. These externalsystems may control the operation of the manipulator 100 and/or collectand process the data gathered by the scanning operation of themanipulator 100. FIG. 1 shows a motor connector 130 used to supply powerand control data to the motor 128 operative to drive the carrier 102around the cuff 106. Probe data connectors 108 serve to communicateultrasound probe control data and data collected by the probe betweenthe probe array and the external data processing systems and/orcontrollers. In other embodiments, some or all of these functions maytake place within the manipulator 100 itself, for example by means of anembedded controller and/or data storage and processing unit. In someembodiments, the motors used by the manipulator 100 may include theirown power sources.

FIG. 2 shows a similar embodiment to FIG. 1 in isolation instead offitted to a pipe. The inner surface 112 of the cuff 106 is visible, asis the outer surface 114. The hose intake 132 is also visible in thisfigure.

FIG. 3 is a side view of the manipulator 100 fitted to a curved pipe,showing the longitudinal axis 4 of the pipe portion being scanned. FIG.4 is an isometric view of the manipulator 100 fitted to a straight pipe,showing the longitudinal axis 4 of the pipe.

The manipulator 100 may in some embodiments be fitted or removed from apipe or other scanning subject by means of a hinged design that allowsthe cuff 106 to be opened. FIG. 5 shows an example embodiment comprisinga hinged manipulator, with a hinged portion 116 allowing the cuff to beopened, and a coupling portion 118 allowing the ends of the cuff to becoupled together into the closed operational position by coupling meanssuch as a latch. FIG. 6 shows the structure of the hinged portion 116,which uses a hinged knuckle 120 to create a double hinge between the twohalves of the cuff 106 rather than a simple, single-hinged clamshelldesign. The hinged knuckle 120 attached to a first half of the cuff 106at a first connecting point 122, and to the second half of the cuff 106at a second connecting point 124. Use of a double hinge allows themanipulator 100 to be more easily placed around a pipe circumference dueto the greater degrees of freedom afforded. The coupling portion 118 isshown in the example embodiment of FIG. 6 as a latch 128.

In operation, the manipulator 100 uses a linear array of ultrasoundprobe elements, such as resonator crystals, to scan the volumeencompassed by the cuff 106. FIG. 7 shows an example linear ultrasoundprobe array 200 having n elements 202. In operation, the linear array200 attached to the carrier 102 is aligned parallel to the longitudinalaxis 4 of the pipe 2 being scanned. The pipe 2 is scanned by the fullarray 200 using the Full Matrix Capture technique described below, thenthe carrier is moved about the circumference of the pipe 2 by a motorincluded in the manipulator 100, after which the scanning process isrepeated for the new circumferential coordinates of the carrier's newposition. By performing a number of such scans at regularly-spacedintervals about the circumference of the pipe slice encompassed by thecuff 106, a model of the entire pipe circumference can be built usingthe scan data.

Full Matrix Capture (FMC) Data Collection

The Full Matrix Capture (FMC) technique used in some embodiments is aknown refinement of the phased-array data capture technique widely usedfor ultrasound scanning FMC generally requires capturing a larger volumeof data than a comparable phased-array scan, but allows more informationto be extracted from a single scan. In Full Matrix Capture, a singleelement 202 of the ultrasound array 200 is pulsed, transmittingultrasound energy into the medium being scanned. Each 202 element of thearray 200 is used as a receiver for this energy, detecting ultrasoundvibrations at its coordinates over the time period following this pulse.This detected vibration is recorded and stored for post-processing. Oncethe data has been recorded for all n elements 202, a second element 202is pulsed, and the recording process is repeated for all receivingelements 202. This process then repeats again, with each of the nelements 202 being pulsed in turn and data recorded for each receivingelement 202, resulting in an n by n matrix of recorded data: eachreceiving element 202 records scan data from the pulse from eachtransmitting element 202. This matrix is illustrated by FIG. 8, showinga matrix of n transmitting elements 206 by n receiving elements 204.

In some embodiments, the data from each receiving element 202 isrecorded as a series of digital samples taken over time. FIG. 9 shows athree-dimensional matrix of such scan data from a singletransmit-receive cycle as described above. The data signal 214 resultingfrom the pulse of transmitter i 210 captured by receiver j 212 is shownas a series of m samples taken over the time dimension 208, resulting ina total three-dimensional matrix of samples n by n by m in size.

In an example embodiment using the manipulator 100 of FIGS. 1 to 6, themovement of the carrier 102 and the operation of the ultrasound array200 is controlled by an external controller connected to the manipulator100 by the data connections 108. Data recorded by the array 200 is sentto an external data recorder and processor via the data connections 108,where it is stored and processed as further described below. Thecontroller and data processor may also be in communication with eachother, and the recorded data may be used by the controller to calibrateor optimize the operation of the carrier 102 and/or array 200 duringscanning.

A single transmit-receive cycle as described above results in n times nA-scans (i.e., time-domain signals received at a receiving element 202).A single A-scan is generally created by a receiving element bymonitoring for vibrations above a set threshold, then recording sensedvibrations and for a set period of time after this threshold is crossed.In some embodiments, a buffer is used to store the sensed data prior torecording, and the recording period is set to include buffered data fora predetermined period before the threshold is crossed, therebycapturing a period beginning shortly before the threshold is crossed andlasting for a set period of time. FIG. 68 shows an illustration of thisprocess. The sensed data 6800 is sampled until a peak 6804 is detectedthat exceeds a predetermined threshold 6806, thereby signalling thebeginning of the period of interest of the signal. However, there mayexist data of interest prior to the peak 6804, such as the initialoscillations of the signal 6800 beginning at point 6802. In order tocapture these initial data points that precede the peak 6804, buffereddata points are retained extending back for a predetermined periodbefore the peak 6804, such as back to an earlier point 6808 which isearly enough to capture any initial signal perturbations of interest.

Processing of FMC Data

Processing of the captured data may be done concurrently with the scanor afterward. Techniques for processing the captured data are describedbelow in accordance with example embodiments. These techniques mayinvolve application of the Shifting Aperture Focusing Method (SFM), theInterior Focus Method (IFM), and boundary detection and recognition todetermine the structure of a scanned object, such as the inner and outersurface contours of a pipe wall. These techniques may allow thedetection of subtle variations in pipe thickness, defects in pipe walls,and other structural details of arbitrary inner and outer surfaces of apipe. Some of the mathematical principles applied by various embodimentsare hereby described to more fully explain their operation.

FIG. 17 is a flowchart showing the operations involved in modeling theouter diameter (OD) and inner diameter (ID) surfaces of a scannedportion of a pipe wall or other object according to an exampleembodiment. The Full Matrix Capture (FMC) data from a transmit-receivecycle of the probe array 200 is collected at step 1702. At step 1704,the raw FMC data is pre-processed. The OD boundary is modeled in steps1706 through 1710, then this OD boundary definition 1712 is used todetermine the ID boundary 1724 in conjunction with the raw FMC data insteps 1716 through 1722. These steps are described more fully below. Insome embodiments, the raw FMC data used includes data from multipletransmit-receive cycles, which is used to improve the modeling ofadjacent radial positions of the pipe wall.

FIG. 10 shows the vector notation used in describing the processing ofA-scan data. The figure depicts a plane defined by the linear ultrasoundarray 200 and the longitudinal axis 4 of the pipe 2 being scanned. Thearea of the plane being scanned in this example is shown by image area14.

k is a vector valued function such that k(u)=<x,y>=<f(u),g(u)> is avector in 2-space, R2, where uε[u1,u2]. The point k(u) is defined ask(u)=(x,y)=(f(u),g(u)). The vector k(u), represented in boldface, isreferred to distinctly from the point k(u). Throughout this description,for any vector v=<x,y>, the point (x,y) is denoted by v, in italics andnot boldface.

K 10 is the curve defined by the set of all points k(u), uε[u₁,u₂]. K ispiecewise-smooth and non self-intersecting. The endpoints of K are k(u₁)and k(u₂) which are denoted k₁ 22 and k₂ 24 respectively.

K separates a first medium 6 from a second medium 8 where ultrasoundprobe element i 210 lies in the first medium 6 and the endpoint ofvector r 16 lies in the second medium 8. For example, when scanning apipe circumference, the first medium 6 would be composed of water pumpedinto the interior volume between the cuff 106 and the outer surface ofthe pipe 2, while the second medium 8 would be the metal of the pipewall itself. The speed of sound in the first medium 6 and the secondmedium 8 are denoted as c1 and c2 respectively.

With reference again to FIG. 10, the figure shows vector k_(o), definedby ko=k(uo)=<f(uo),g(uo)>, where the point koεK (point k_(o) beingdenoted by numeral 18, surface K by numeral 10). Also, ultrasoundelement i 210 is shown with position vector e(i) from coordinate origin20. The travel time from ultrasound element i 210 to r 16 through ko 18,denoted by t_(ir) ^(ko), is given by the following equation:

$\begin{matrix}{t_{ir}^{k_{0}} = {\frac{{k_{0} - e_{(i)}}}{c_{1}} + \frac{{r - k_{(0)}}}{c_{2}}}} & \left( {{Equation}\mspace{14mu} 1} \right)\end{matrix}$

The travel time from r 16 to i 210 through k_(o) 18 (denoted)t_(ri)^(ko) is equal to t_(ir) ^(ko). The times from i 210 to r 16 through allkεK, k varying parametrically from k₁ to k₂, are given by the curve,T_(ir)(K) 26, illustrated in FIG. 11.

Physical Considerations Speed of Sound Variation—Material PhysicalConsiderations

In ultrasonic applications, absolute distance measurements may bedirectly calculated from the travel times of acoustic pulses, and hencemay be sensitive to the speed of ultrasonic sound in the materials understudy. In some embodiments described below pertaining to pipe weldinspection, thickness is defined as the shortest distance from a pointon the outer surface to the inner surface. FIG. 72 illustrates thisdefinition of wall thickness. L1 and L2 are the thicknesses at twodifferent locations on the outer surface of a feeder weld. The inner andouter surface profiles are essential pieces of information used todetermine the wall thickness with respect to any location on the outeror inner surface.

The ultrasonic distance measurement is determined by the sound velocityof the material and the time taken for a sound wave to travel betweenstart and end points. A distance measurement L can be written as L= Vτwhere τ is the time taken by the wave to travel from start and endpoints. V is the average sound velocity along the path. Since the outersurface profile may be measured using immersion techniques and water maybe used as a couplant in some embodiments, the sound velocity of waterwould need to be considered, as it would affect the distant measurementaccuracy.

Water Sound Velocity Temperature Dependency

The sound velocity of water as a function of its temperature isV_(fw)(T)=1405.03+4.624 T−0.0383 T² where V denotes the fresh watersound velocity in meters per second unit and T denotes the temperaturein degrees Celsius.

Element Directivity (Beam Spread—Lateral and Transverse)

Element directivity is a potentially important factor in the design ofthe probe array. Generally speaking, element directivity can be thoughtof as the variance of the amplitude pressure field across differentpoints on the inspection volume. Both the Cartesian (x-y-z) andspherical (φ−θ−r) coordinates are standard when discussing directivity(see FIG. 73). Convenience dictates that the Cartesian coordinates areof use when discussing near field element directivity while sphericalcoordinates are of use when discussing far field directivity. In the farfield, for a rectangular element with L much longer than a, and for agiven distance, r, the directivity in the x-z plane is well approximatedvia a function of only θ, excitation frequency f, and a.

Element directivity is ultimately derived from the wave equation, givenby the following:

${\frac{\partial^{2}p}{\partial x^{2}} + \frac{\partial^{2}p}{\partial y^{2}} + \frac{\partial^{2}p}{\partial z^{2}} - {\frac{1}{c^{2}}\frac{\partial^{2}p}{\partial t^{2}}}} = 0$

where p is pressure and t is time.

The pressure field at a given point in an inspection volume can bederived numerically. A transducer can be treated as a piston radiatingsound waves in water where the transducer generates an infinite numberof plane waves, all traveling in the positive z-direction but withdifferent x and y component directions. As such, the pressure field at apoint q=(x; y; z) is represented in the form of a 2D integral given by:

${p\left( {q,\omega} \right)} = {\left( \frac{1}{2\pi} \right)^{2}{\int_{- \infty}^{\infty}{\int_{- \infty}^{\infty}{{P\left( {k_{x},k_{y}} \right)}^{{({{k_{x}x} + {k_{y}y} + {k_{z}z}})}}{k_{x}}{k_{y}}}}}}$

where k=(k_(x),k_(y),k_(z)) is the wave vector with magnitude

$k = {\frac{2\pi}{\lambda}.}$

After some derivation, a solution to the above equation for arectangular element with length l_(x) in the x direction and lengthl_(y) in they direction can be derived where:

${p\left( {q,\omega} \right)} = {\left( \frac{1}{2\pi} \right)^{2}{\int{\int_{{k_{x}^{2} + k_{y}^{2}} \leq k^{2}}{\frac{\; \omega \; \rho \; {V\left( {k_{x},k_{y}} \right)}}{\; k_{2}}^{{({{k_{x}x} + {k_{y}y} + {k_{z}z}})}}{k_{x}}{k_{y}}}}}}$

where V(k_(x),k_(y)) is the 2D spatial Fourier transform of the velocityof the field of the transducer:

V(k _(x) ,k _(y))=∫_(=∞) ^(∞)∫_(=∞) ^(∞) v _(x)(x,y,z=0,ω)e ^(−i(k) ^(x)^(x+k) ^(y) ^(y)) dxdy

Far Field Directivity

In the far field of an element (when the ultrasound transducer elementdimension is very small with respect to inspection distance), for agiven radius r in the x-z plane, the directivity can be wellapproximated by a function varying only in θ, element width a, andfrequency f, when the transducer length L is much bigger than its width,a. Far field directivity may be relevant in some embodiments involvingweld inspection because the weld will often be in the far field (in theaxial direction, x-z plane) of the ultrasound array transducer elements.The formula for far field directivity of the element in the x-z plane isgiven below:

${D_{F}\left( {f,\theta} \right)} = {{sinc}\left( \frac{\pi \; {af}\; \sin \; \theta}{c} \right)}$

It is worth noting that both a and f increase directivity at a givenangle θ. FIG. 74 illustrates directivity in an example embodiment havingθ=−90 degrees to 90 degrees, a=0.23 mm, f=7.5 MHz, and c=1480 m/s.

Element Width

Element width a dictates element directivity as is illustrated in theequation above. Smaller element widths will radiate soundomnidirectionally (in all directions). Larger element widths will focussound in the direction of their surface normals. This is illustrated byFIG. 75, which shows element directivity as a function of element sizea.

Analogously, with respect to focusing arrays, arrays with larger elementwidths will generally focus better than arrays with smaller widths inthe direction of the array normal. The direction of the array normalcorresponds to a steering angle of zero. On the other hand, arrays withsmaller element widths will generally focus better (than arrays withlarger element widths) in directions away from the array surface normal.

Both claims can be confirmed by examination of FIG. 76( a) and FIG. 76(b). Both figures simulate the same array with:

-   -   Number of Elements=10    -   Frequency=7.5 MHz    -   Pitch (spacing between centre of elements)=0.28 mm

While the element width a is varied between 0 and the pitch (0.28 mm),FIG. 76( a) simulates the array directivity where the steering angleθ_(s) equals 0 degrees while FIG. 76( b) simulates the array directivitywhere the steering angle θ_(s) equals 30 degrees. The intensity of themain lobe at different element widths is visible by examining the lineθ_(s)=0 for both figures, while the other horizontal lines represent theundesirable effects of grating. For θ_(s)=0 (FIG. 76( a)), largerelement widths attenuate the effects of grating while preserving thedesirable main lobe intensity. Conversely, for θ_(s)=30 (FIG. 76( b)),smaller element widths preserve the main lobe intensity while gratinglobe intensity is unvaried by element size.

Element Elevation

Element elevation is represented as L in FIG. 73. In the far field, alarge L (with respect to a) will focus energy in the direction normal tothe surface (the z direction) while smaller values for L will radiateenergy with larger components in y. In the near field, however, which isgenerally of interest when inspections are performed at distancescomparable to L, energy radiated from the transducer will be projecteduniformly from the surface of the transducer in the z direction. This isconsistent with expectations, as at these inspection distances,inspection points will be comparable to the elevational focal length ofthe transducer.

Recall the coordinate system of FIG. 73. FIG. 77( a) and FIG. 77( b)reveal the pressure field from a 0.25 mm width, 5 mm elevationtransducer radiating a 7.5 MHz continuous sine wave intersecting on twoplanar spaces P₁={x=0, 0≦y≦5, 0≦z≦20} and P₂={x=1, 0≦y≦5, 0≦z≦20} (alldimensions are in millimetres). The first space P₁ is a plane spanned byvectors in they and z direction and bounded at y=0.5, and z=0.20. SpaceP₂ is spanned and limited in the same manner as P₁, but is spaced 1 mmaway from P₁. Both spaces are shown in FIG. 75. FIG. 77( a) and FIG. 77(b) illustrate that the intensity of the pressure field radiated by thetransducer is concentrated in the y<2.5 mm area for both casessimulated. The symmetry of the radiated field is exploited in thesimulations given. If the radiated pressure field was simulated atplanar spaces P′₁={x=0, −5≦y≦0, −20≦z≦0} and P′₂={x=1, −5≦y≦0, −20≦z≦0}which are reflections of planar spaces P₁ and P₂ about the z-x plane,the intensity field simulated on these spaces would be a reflection ofthose on P₁ and P₂ about the z-x plane.

Quantization Noise

In analog-to-digital conversion, the magnitude of each data sample isconverted into an approximated value with finite precision. Quantizationis a non-linear process. The smallest quantization level is theresolution. It is determined by the full-scale input amplitude of ananalog/digital (A/D) converter and the total number of quantizationlevels which are usually evenly spaced. The resolution is oftenexpressed by the number of quantization level. A 10-bit A/D converterhas 1024 quantization levels. A 12-bit A/D converter has 4096quantization levels. The resolution of a 12-bit AID converter is fourtimes smaller than that of a 10-bit A/D converter. Since thequantization error is expected to be smaller than the smallestquantization level, a higher resolution A/D converter is in generalpreferred.

If the quantization process rounds the input data value to the nearestquantization level and the errors obey the even-statistical distributionover the quantization intervals, the mean value of the quantizationerror is clearly zero. The variance of the quantization error is givenby

$\sigma_{e}^{2} = \frac{\Delta^{2}}{12}$

where Δ is the quantization interval.

The root-mean-squared (rms) value of the quantization error is thestandard deviation

$\sigma_{e} = {\frac{\Delta}{\sqrt{12}} \approx {0.29\; {\Delta.}}}$

I we define a SNR to be the ratio of signal variance to the noisevariance, the SNR of a B+1 bit A/D converter can be expressed in someembodiments as:

${S\; N\; R} = {{6.02B} + 10.8 - {20\; {\log_{10}\left( \frac{X_{m}}{\sigma_{x}} \right)}}}$

where X_(m) is the full scale level of the A/D converter, and σ_(x) isthe standard deviation of the signal.

The SNR limits for 8-bit, 10-bit and 12-bit in this example embodimentare 50 dB, 62 dB and 74 dB respectively. It is worth noting that theoptimum SNR can generally only be achieved when the input signal iscarefully adjusted to the full-scale amplitude of the A/D converter.

Preprocessing

In some embodiments, preprocessing consists of several operations whichcondition raw data for analysis via the SFM and IFM subroutines. In anexample embodiment shown in FIG. 18, these operations are as follows:upsampling the full matrix capture raw data to a sampling frequency of100 MHz 1804, subtracting the DC offset from the full matrix capture rawdata 1806, filtering the full matrix capture data set to remove unwantednoise 1808 using digital software filter coefficients 1810, andcalculating 1812 the analytic time-domain full matrix capture data-set1814 from the acquired RF full matrix capture data set 1802.

In this example embodiment, full matrix capture (FMC) raw data 1802 iscollected by the acquisition system at a sampling frequency of 50 MHz.Analyzing the raw data collected at this frequency may deliver resultswith insufficient accuracy. For this reason, the raw data is upsampledto 100 MHz at step 1804. The acquisition system in this embodiment issensitive to frequencies less than 25 MHz, and data is collected attwice this rate, so due to the Nyquist Sampling Theorem, the raw datacan be perfectly reconstructed at any sampling rate above 50 MHz. TheFMC raw data is upsampled from 50 MHz to 100 MHz.

The acquisition system stores raw data via a 12-bit quantization schemewhere only positive values are stored. In this embodiment, FMC waveformsare centered about 2¹²/2=2048. The analysis algorithms require thatwaveforms be centered about zero. In some embodiments, the theoreticalDC offset value of 2048 may not be exactly accurate: there may be a DCoffset inherent in the hardware controlling the ultrasound probe array.Experimentation may show the right DC offset value to use to get a zerovalue as closely as possible to reality; in some embodiments usingspecific hardware, the value is 2058. Therefore a DC offset of 2058 issubtracted from each FMC waveform. The exact value of the DC offset maybe a user configurable parameter which can be changed to account forother deviations from the theoretical value. The DC offset is subtractedat step 1806.

Unwanted frequency content in the full matrix capture data willsometimes be present due to various noise contributions. Thesefrequencies can be attenuated at step 1808 through the utilization of adigital software filter. Software filtering coefficients may bespecified in the filtering process, such as parameters derived from theFilterbuilder program in the Matlab™ software application, at step 1810.

Assigning intensities to points in the inspection medium requires thefull matrix data-set of analytic time-domain signals. The full matrixdata-set output of the acquisition system 1802 contains the RF data-set(the real part of the analytic time-domain signals). To compute theanalytic time-domain signals 1814 from the RF data-set at step 1812, theHilbert transform of the RF data-set is calculated, multiplied by theimaginary number, i, and added to the RF data-set.

Detailed descriptions of functions used in an example embodiment of thesystem are set out in Table A1 at the end of the Description.

Shifting Aperture Focus Method (SFM)

Once pre-processing 1704 is done, the Shifting Aperture Focus Method(SFM) is applied to the pre-processed data at step 1706. The ShiftingAperture Focus Method is an algorithm whose purpose is to output anintensity map given full matrix capture raw data. The operation of theSFM to determine the OD intensity map is shown in the flowchart of FIG.20. The pre-processed FMC data 2004 may or may not be normalized first,depending on the embodiment or on user-defined parameters forprocessing. The decision to normalize is made at step 2010. If the data2004 is to be normalized, normalization occurs at step 2012 as furtherdescribed below, based on OD normalization parameters 2006 eitherpre-defined or set by a user. The normalized or non-normalized data isthen used to calculate inspection coordinate travel times at step 2014as further described below, a process which may take into accountintensity coordinates calculated at step 2016 based on OD imagingparameters 2002. At step 2018, the intensities at the current inspectioncoordinates are calculated. At step 2020, the inspection coordinates andtheir respective intensities are stored. At step 2022, the algorithm mayfocus further around high intensity coordinates; if it does, theintensity coordinates are calculated at step 2016, creating an iterativeloop for steps 2014 to 2022. When the algorithm has iterated throughthis process one or more times, it stops re-focusing and outputs an ODintensity map 2008. These various steps are described below.

The Shifting Aperture Focusing Method is a variation on the TotalFocusing Method (TFM). The Total Focusing Method is a known techniquefor imaging in a single medium where sound travels at speed c.

In the Shifting Aperture Focusing Method, first, an intensity functionis computed for each point r in the scanning area by summing a functiong_((i)j)(t) for each transmitting element i and each receiving element jwithin a fixed-width aperture a spanning a set number of adjacentelements in the array. (In some embodiments, the width of the definedaperture may be user-configurable.) Ultrasound elements i 210 and j 212belong to aperture a. g_((i)j)(t) is the amplitude of the data-set ofanalytic time-domain signals from transmitter i 210 to receiver j 212 attime t (note that g_((i)j)(t) is defined for every i and j, since thefull matrix of ultrasonic transmit-receive array data is acquired). i210 is enclosed in parentheses to represent it as the transmitter, whilej 212 is left without parenthesis to represent it as the receiver. Theintensity at r is defined as:

$\begin{matrix}{{I\left( {r,a} \right)} = {{\sum_{i,{j \in a}}{g_{{(i)}j}\left( {t = \frac{{{e_{(i)} - r}} + {{e_{j} - r}}}{c}} \right)}}}} & \left( {{Equation}\mspace{14mu} 2} \right)\end{matrix}$

where r is the vector defining point r relative to a coordinate origin,e_((i)) is a vector defining the position of transmitter i relative tothe coordinate origin, e_(j) is a vector defining the position ofreceiver j relative to the coordinate origin, and c is the speed oflight.

Total focusing is achieved by calculating the above for every point inthe imaging area 14 (r is varied). By varying r over a set of pointssufficiently close to each other such that I(r, a) does not varysignificantly between adjacent values of r, an image of the inspectionmedium can be formed with image pixels of intensity I(r, a) at positionsr. This image is called an intensity map.

The next step in the Shifting Aperture Focusing Method is to shift theaperture a along the array and perform the same calculation again forthe new aperture. After intensities have been computed for eachaperture, the highest such intensity value is used to represent theintensity of point r in the intensity map. Thus, SFM addresses theproblem of how to relate intensities from different apertures inassigning an intensity value to r. In many instances, a surface containsreflectors that vary with respect to which apertures they reflect soundbest to. For example, one reflector may return sound well to aperturesa₁ through a₆, while another reflector may return sound well to onlyaperture a₃. In imaging a surface, however, reflectors should be imagedwith equal intensity, irrespective of how many apertures individualreflectors reflect sound well to. This will lead to increased intensitylevelness in imaging the surface. To this end, I(r) is defined as themaximum intensity at r of the set of computed intensities at r withrespect to apertures aεA. I(r) is defined as:

$\begin{matrix}{{I(r)} = {\max\limits_{a \in A}\left\{ {I^{\prime}\left( {r,a} \right)} \right\}}} & \left( {{Equation}\mspace{14mu} 3} \right)\end{matrix}$

Implementation of the SFM routine can become very computationallyintensive if there are many coordinates for which correspondingintensities are evaluated. Limiting the number of coordinates underconsideration, while focusing at the appropriate density to meetinspection specifications, may require careful implementation of afocusing strategy. The strategy employed in some embodiments is to firstcalculate intensities of coordinates on a course grid lying in the areaof inspection.

Detailed descriptions of OD Imaging Parameters used in an exampleembodiment of the system are set out in Table A2 at the end of theDescription. Detailed descriptions of SFM Functions used in an exampleembodiment of the system are set out in Table A3 at the end of theDescription.

FMC Data Normalization

The Total Focusing Method and Shifting Aperture Focusing Method (as wellas a further variation, the Interior Focusing Method, described below)may use Beam Normalization. Beam-steering at angles away from thedirection normal to an array may be optimized when individual elementsare omnidirectional. A correction factor may be introduced in imaging toemulate beam-spread omnidirectionality. A potential problem with thismethod is that it presumes signals found in g(i)_(j)(t) are located inthe direction of r, when assigning a value to I(r,a). While this may notbe a problem of great concern when attempting to image small objects,when imaging surfaces this may lead to amplified grating. Amplifiedgrating causes a reduction in signal to noise ratio in areas of theimage where the true surface and grating overlap. The method presentedhere for normalizing beam-spread is to normalize wave-packets found inthe real and imaginary parts of g(i)_(j)(t) such that the envelope ofg(i)_(j)(t),|g(i)_(j)(t)|, has peak(s) equal to an arbitrary constant.For simplicity, in this example this constant is equal to 1. Thus, ifg(i)_(j)(t) contains wave packets W={w₁, w₂, . . . , w_(n)} and|g(i)_(j)(t)| has peaks P={p₁, p₂, . . . , p_(n)}, to normalize thepeaks of |g(i)_(j)(t)| to 1, the wave packets W are scaled by {p₁ ⁻¹, p₂⁻¹, . . . p_(n) ⁻¹}. Let g′(i)_(j)(t) denote g(i)_(j)(t) with normalizedwave packets. FIG. 15 and FIG. 16 illustrate this concept of analytictime domain signal normalization. The A-scan data 1500 in FIG. 15 has areal part 1502 that exhibits a first peak 1506 and a second peak 1508.The envelope 1504 of the signal is higher for the first peak 1506 thanfor the second peak 1508. After applying wave packet normalization asdescribed above, the normalized A-scan 1600 is shown in FIG. 16. Thereal part 1602 of the A-scan has had the envelope 1604 of its first peak1606 and its second peak 1608 normalized to the same constant value.

SFM Refocusing

At step 2022, depending on preset or user defined parameters, the SFMsubroutine either ends, outputting the intensity map 2008 to theboundary detection subroutine, or proceeds to define new coordinates forwhich to calculate corresponding intensities at step 2016. If the lattercourse is taken, newly defined coordinates will be positioned aroundcoordinates with high intensities already assigned to them. The cutoffintensity for coordinates of which newly defined coordinates focusaround may be defined by a predefined vector. Once the new coordinateshave been defined, potentially depending on user defined parameters, theSFM subroutine exits, or proceeds to further focus around coordinates ofhigh intensity. The process of identifying high intensity coordinatesand then refocusing around them can be executed an arbitrary number oftimes, which may be specified by a user in some embodiments.

One cycle of the refocusing process (steps 2014 to 2022) is illustratedin FIG. 19. Coordinates spaced coarsely apart are represented as eitherwhite circles 1902 or black circles 1904. The white circles 1902represent those coordinates whose respective intensities are below thecutoff intensity for which new coordinates are defined. Conversely, theblack circles 1904 represent those coordinates whose respectiveintensities exceed the cutoff intensity for which new coordinates aredefined. The gray circles 1906 represent newly defined coordinatesaround high intensity coordinates. If the gray circles 1906 are definedon iteration i+1 of the coordinate definition and intensity assigningprocess, dx(i)/dx(i+1)=dz(i)/dz(i+1)=4 for the example illustrated byFIG. 19 with x-axis 1908 and z-axis 1910.

Intensity Maps

An example intensity map of the OD (outer surface) of a pipe 2 is shownin FIG. 12. The OD intensity map 50 is mapped within the same plane asFIG. 11, with the depth 28 of the scan as the vertical axis and theaxial position 30 along the longitudinal axis 4 as the horizontal axis.The high-intensity OD regions 32 in the OD intensity map 50 indicate theshape of the outer pipe surface in the axial direction at the radialposition of the probe array 200 during the current transmit-receivecycle.

Boundary Recognition and Definition

The OD intensity map 50 can be further processed, either on its own orin conjunction with neighbouring intensity maps from separatetransmit-receive cycles, to build a model of the outer pipe surface. Inthe example embodiment shown in FIG. 17, boundary recognition isperformed at step 1708 followed by boundary definition at step 1710.

Boundary recognition is executed as follows. Given an intensity map(stored in sparse coordinates in some embodiments) of the OD coordinatesor an intensity map of the ID coordinates along with correspondingboundary recognition parameters, the boundary recognition algorithm willoutput the surface boundary.

Boundary recognition and definition are intended to define the trueboundary of the OD/ID (not edges of aberrations in the image), and theextraction of the boundary in the form of a set of coordinates. Thealgorithms and tolls used to accomplish this task are common challengesin the field of computer vision, and any of a number of algorithms couldbe employed to recognize and define the boundary of a surface given anintensity map.

In some embodiments, the tools used for these tasks are a robust edgedetection algorithm, various morphology operations, and association ofhigh intensity regions of the intensity map with potential boundaries.These tools are described in detail below.

In general, an image is given by A=f(m,n), where pixel (m,n) is locatedat row m and column n of the image. Both m and n are elements of the setof integers, Z. f is a function outputting a real number (an element ofthe set R) such that f:Z²→R. If we restrict f(m,n) to values of either 0or 1, then the image f(m,n) is referred to as a binary image.

Example Algorithms Used in Boundary Recognition and Definition

The following portion of the specification defines some basic setoperations on images, which form the groundwork for higher leveloperations defined in subsequent subsections. We first introduce thetranslation and reflection operations. For an image A, the translationof A by x=(p,q) is represented by A and is defined as

A _(x) ={f(m+p,n+q)|f(m,n)εA}  (Equation 4)

The reflection operation, denoted Â, is given as

Â={f(−p,−q)|f(p,q)εA}  (Equation 5)

Furthermore, introduced are the fundamental union and intersectionoperators, represented by ∪ and ∩ respectively. The union of two imagesA=f(m,n) and B=g(m,n) is defined as

A∪B={max(f(m,n),g(m,n))|f(m,n)εA,g(m,n)εB}  (Equation 6)

whereas the intersection of the two images A and B is defined as

A∩B={min(f(m,n),g(m,n))|f(m,n)εA,g(m,n)εB}  (Equation 7)

Regions of adjacent images which share the same values are identified asconnected components. While there are different measures ofconnectivity, the present described example embodiment considers pixelswhich are 8-connected. If any two pixels are adjacent to one another(including diagonally adjacent), then they are considered 8-connected.

Dilation and Erosion

Different algorithms exist for labelling connected components of animage. In general, given an image A, a new image B can be defined suchthat the values of its pixels are the labels of the connected componentsin image A. For a given image of interest, A, a structuring set B,called the ‘structuring element’, is an image which, along with either adilation or erosion operation, is used to modify the image of interest.

The dilation operation is defined as

A⊕B={x|{circumflex over (B)} _(x) ∩A≠0,x=(m,n),m,nεZ}  (Equation 8)

Effectively, the dilation operation enlarges image A by reflecting Babout its origin and then shifting it by x. Erosion on the other hand isdefined as

A⊖B={x|B _(x) ∩A=B,x=(m,n),m,nεZ}  (Equation 9)

Erosion preserves 1's in A which when B is translated by x and isintersected with A, equals B. Erosion has the effect of trimmingboundary l's from an image given an image B which is centered on theorigin.

FIGS. 24 and 25 illustrate the dilation and erosion operations,respectively, performed on the image given in FIG. 22. The structuringelement used in the dilation and erosion operations is a 5×5 matrix,centered on the origin, given in FIG. 23.

Edge Detection

Edge detection can be performed using an edge detection algorithm asknown in the art. One of the most popular, robust, and versatile edgedetectors used is the Canny Edge detector. It is described in detailbelow.

The first step in Canny edge detection is to smooth the input image. Thepurpose of this smoothing is to reduce noise and unwanted details andtextures. The smoothing process is performed via convolution of theimage with a two dimensional Gaussian function. Where the image is givenby f(m,n), and the Gaussian is given by Gσ(m,n), the convolution isgiven by

g(m,n)=f(m,n)*Gσ(m,n)  (Equation 10)

The particular two dimensional Gaussian used here is of the form

$\begin{matrix}{{G\; {\sigma \left( {m,n} \right)}} = {A\; ^{- {({\frac{m}{2\sigma_{m}^{2}} + \frac{n}{2\sigma_{n}^{2}}})}}}} & \left( {{Equation}\mspace{14mu} 11} \right)\end{matrix}$

where σ_(m) and σ_(n) are variances of the Gaussian in the vertical andhorizontal directions respectively. In traditional implementation ofCanny edge detection, σ_(m)=σ_(n), yielding a simpler form of Equation11. However, in the present described example embodiment, spacingbetween vertical pixels and horizontal pixels is not necessarily thesame. For example, pixels adjacent to each other in the verticaldirection may represent locations 0.01 mm apart, while pixels adjacentto each other in the horizontal direction may represent locations 0.015mm apart. Therefore, if a Gaussian is used in the convolution processwhich is circular as opposed to elliptical in shape (not stretched ineither the vertical or horizontal directions) with respect to actuallocations of pixels, σ_(m) will not be equal to σ_(n) if spacing betweenvertical pixels and horizontal pixels is different. If the spacingbetween pixels in the vertical direction is a factor if c times as muchas spacing between pixels in the horizontal direction, thenσ_(m)=σ_(n)/c.

The second step in the edge detection process is to calculate thegradient of the convoluted image, g(m,n). Since g(m,n) is a discretefunction, and generally non-analytic, operators have been developed toapproximate the gradient of g(m,n), ∇g(m,n). The details of theseoperators are not described here. Utilization of any of the abovementioned operators produce g_(m)(m,n) and g_(n)(m,n) which are twoimages containing vertical and horizontal gradient approximations. Thusthe magnitude and direction of ∇g(m,n) may be represented as M(m,n) andθ(m,n), where

$\begin{matrix}{{M\left( {m,n} \right)} = \sqrt{{g_{m}\left( {m,n} \right)} + {g_{n}\left( {m,n} \right)}}} & \left( {{Equation}\mspace{14mu} 12} \right) \\{{\theta \left( {m,n} \right)} = {\arctan \left( \frac{g_{m}\left( {m,n} \right)}{g_{n}\left( {m,n} \right)} \right)}} & \left( {{Equation}\mspace{14mu} 13} \right)\end{matrix}$

Step three is to calculate those pixels in M(m,n) which are localmaxima. To do this, for every pixel (m,n), two most neighbouring pixelsperpendicular to the direction θ(m,n) are considered. If the value of Mat these pixels are both less than that of M(m,n), then (m,n) isconsidered to be a local maximum. The set of all local maxima in M(m,n)is denoted as {tilde over (M)}(m,n).

Step four is to define two binary images as functions of two respectivereal numbers, τ₁ and τ₂, respectively, where 0<τ₁<τ₂<τ′. τ′ is equal tothe maximum value of M(m,n). {tilde over (M)}τ¹(m,n) and {tilde over(M)}τ²(m,n) are defined as being images containing values of {tilde over(M)}(m,n) greater than τ₁ and τ₂ respectively. Thus,

{tilde over (M)}τ ¹(m,n)=[(1 if {tilde over (M)}(m,n)>τ_(i))₅(0otherwise),i=1,2]

The last step (step five) is an iterative process. E_(j) is a Booleanimage containing edges, output at iteration j of the process. F_(j) isthe intersection of those pixels adjacent (in an 8-connected fashion) topixels with value 1 in E_(j) and pixels with value 1 in {tilde over(M)}τ¹(m,n).

Set j=1 E₁ as {tilde over (M)}τ² (m,n). The iterative computationbegins. If the union of E_(j) and F_(j) is equal to E_(j), then thealgorithm terminates, outputting E_(j) as the canny detected edge. Ifnot E_(j) is defined as the union of E_(j) and F_(j), and j isincremented. The algorithm then loops, testing the equality of E_(j) andE_(j)∪F_(j), until they are equal. The algorithm outputs E_(j) when itis equal to the union of E_(j)∪F_(j).

This can be represented in pseudo-code as follows:

j=1; E_(j) := {tilde over (M)}τ²(m,n); While E_(j) ∪ F_(j) != E_(j) doE_(j) := E_(j) ∪ F_(j); j=j+1; end output E_(j)

FIG. 26( a) shows an image of a set of coins, while FIG. 26( b) showsthis image processed with Canny edge detection.

Thinning

A thinning algorithm may also be used in boundary detection. Reducingconnected components in an image to their thin-line representation has anumber of useful applications such as data compression, simplestructural analysis, and elimination of contour distortion. Oneiterative thinning used in an example embodiment is presented here. TheA, V, and symbols represent and, or, and not operations respectively.

Given an image A=f(m,n), the first step in the thinning algorithm is todivide it into two subfields in a checkerboard pattern. The algorithmthen uses a parallel approach where the following two sub-iterations areexecuted in parallel repeatedly, until they have no effect on the image.

The first sub-iteration is to delete pixel p in the checkerboard if andonly if conditions G₁, G₂, and G₃ are all satisfied.

The second sub-iteration is to delete pixel p in the checkerboard if andonly if conditions G₁, G₂, and G₃′ are all satisfied. Conditions G₁, G₂,G₃, and G₃′ are listed as follows:

Condition G₁ is:

X _(H)(p)=1

where

X _(H)(p)=Σ_(i=1) ⁴ b _(i) and

b _(i)=[(1 if x _(2i-1)=0

(x _(2i-1)=1

x _(2i+1)=1),(0 otherwise)]

x₁, x₂, . . . , x₈ are the values of the eight neighbours of prespectively beginning with the east neighbour and numberedcounter-clockwise until the south-east neighbour is reached.

Condition G₂ is:

2≦min{n ₁(p),n ₂(p)}≦3

where

n ₁(p)=Σ_(i=1) ⁴ x _(2k-1)

x _(2k)

n ₂(p)=Σ_(i=1) ⁴ x _(2k)

x _(2k+1)

Condition G₃ is:

(x ₂

x ₃

x ₈)

x ₁=0

Condition G₃′ is:

(x ₆

x ₇

x ₄)

x ₅=0

FIG. 27( a) shows a binary image without thinning FIG. 27( b) shows thesame image after a thinning algorithm has been applied.

Junction Identification

An algorithm may also be applied to test if a pixel is a junction pointof lines in a binary image. Given a 3×3 neighbourhood of a pixel p, p isa junction of lines if and only if when traversing the perimeter of pthe number of transitions between 0 and 1 is either 6 or 8. FIG. 28( a)gives a junction of lines and FIG. 28( b) zooms in on this junction andillustrates its 3×3 neighbourhood. It is clear from FIG. 28( a) that thejunction has 6 transitions between 0 and 1. Thus, the criterion forjunction identification is satisfied.

Implementation of OD and ID Boundary Recognition and Definition

The algorithm employed to extract OD or ID boundary coordinates from anOD or ID intensity map in example embodiments may employ all the toolsintroduced in the previous subsection and tailor their use to thespecific domain of ultrasound scanning.

The first step to extraction of the true OD/ID boundary from the OD/IDintensity map involves identifying canny edge detected boundaries nearand above (on the side near to the array 200) maximum intensity pixelsin the vertical direction of the intensity map. Given an intensity map,the true boundary may be extracted. The first question that arises iswhere the precise coordinates of the boundary lie, given theneighbourhood of and around high intensity pixels in an intensity map.It can be observed from a given intensity map (such as FIG. 30) thatthere is a relatively wide region of high intensity pixilation where thetrue boundary could lie. To answer this question, the method ofcalculation of distances from A-Scans is extended to intensity maps. Tocalculate the distance an ultrasonic wave has travelled in a medium froman A-Scan, the time from transmitter excitation to the leading edge ofthe received wave packet is multiplied by the speed of sound in themedium. The travel time (in samples at a particular digitizationfrequency) from transmitter excitation to the leading edge of a receivedwave packet is shown by numeral 2902 in FIG. 29. The formation ofintensity maps involves the mapping of values of analytic time-domainsignals (A-Scans along with their Hilbert transforms) to points in theintensity map, per Equation 2. The leading edges of wave packets ofA-Scans are mapped to the leading edge of intensity map boundaries wherethe leading edge of intensity map boundaries are defined as the edges onthe near side of the probe array 200. Thus, the OD/ID boundary isderived from leading edges in the intensity map boundaries.

The second question that arises is how to programmatically identify thetrue OD/ID boundary with the potential of imaging aberrations (featuresappearing due to mechanisms such as grating lobes or multiple back-wallreflections) present in the intensity maps. In FIG. 30, of the edges inthe image, the true boundary is of the edges nearest to the regions ofhigh intensity, contrasting the edges of aberrations due to grating orback wall reflections. Taking any vertical slice of the image, the trueboundary will intersect this vertical slice just above where it willintersect the maximum intensity pixel in that slice. These observationsmotivate the first step of the boundary detection algorithm, isolationof the true boundary edges through identification of edges near andabove the highest intensity pixels in the vertical direction. For agiven intensity map such as the one given in FIG. 30, the edges of theintensity map and the pixels of maximum intensity in the verticaldirection are plotted in FIG. 31. The true boundary edges are those thatare near and above the high intensity pixels and are plotted in FIG. 32.

Identification of the OD/ID boundary solely through the methods employeddescribed above may yield results which can be improved upon. Additionaltechniques may be used in some embodiments to eliminate error in theboundary defined via the methods described earlier above.

Aberrations can have higher intensity content than the true boundary,which can lead to false identification of the true OD/ID boundary. Ifthese aberrations are small in size, however, operations of dilation andthinning, along with comparison of connected component sizes, can beemployed to remove erroneous edges from the boundary definitionextracted from the algorithm previously described. Elimination oferroneous edges at this stage in the boundary detection algorithm may beperformed via three steps: dilation of the candidate boundary, thinningof the dilated boundary, and trimming of small connected components.

Elimination of Erroneous Edges

FIG. 33 to FIG. 36 give the boundary at various stages in the executionof an example algorithm for eliminating erroneous edges. The originalboundary output from the algorithm given above is depicted in FIG. 33.Due to high intensity content in the aberration below the true boundary,the output boundary contains error. This is evident in that a smallregion of the output boundary appears above the aberration. FIG. 34shows the boundary after the dilation operation is performed with arectangular structuring element. The small gap in the location where theboundary should lie (FIG. 33) has been removed Thinning of the boundaryreduces it back to its desired width. The thinned boundary is depictedin FIG. 35. Finally, a comparison of boundary pixels is performed. Ifthere is more than one boundary pixel intersecting any vertical slice ofthe intensity map, only the pixel belonging to the connected componentof largest size (of the pixels under consideration) is preserved. Therest are removed. This serves to remove the erroneous boundary pixels.FIG. 36 gives the boundary with erroneous pixels removed.

Removal of Edge Junctions

The dilation and thinning operations can introduce line junctions aspreviously defined. At this point in the boundary detection algorithm,junctions may be removed, as set out above, until no junctions exist inthe defined boundary. This serves the purpose of preparing the boundaryfor removal of more erroneous edges, performed via the algorithmsdescribed below.

FIG. 37 gives an ID intensity map image. The edge detection, dilationand thinning algorithms give the boundary given in FIG. 38. The junctionarea 3802 of the boundary encircled in FIG. 38 reveals the area on andaround the boundary to be removed as erroneous. This junction area 3802is shown magnified in FIG. 39. FIG. 40 illustrates the junction area3802 of the boundary with the junction removed.

Removal of Bottom Portions of Connected Components

The next phase of the edge detection process is a removal of bottomportions of connected components. Removal of bottom portions ofconnected components consists of removal of lower connected componentpixels. A lower connected component pixel is defined here as a pixel insome connected component having the same x-component as some otherpixel(s) in the same connected component, but with greater z-componentvalues (recall pixels with greater z-component values appear lower onintensity maps. FIG. 41 shows the boundary in FIG. 40 with bottomportions of connected components removed.

Removal of Small Connected Components

Small connected components of erroneous edges can still remain in theintensity map. Removal of these edges increases the accuracy of thedefined boundary. For every vertical strip of the intensity map, if morethan one connected component intersects this slice, then only thelargest one is preserved in the intensity map. FIG. 42 illustrates theedge boundary with small connected components removed. This image is themost accurate of any so far in the identification of the true edgeboundary of the intensity map depicted in FIG. 37.

Approximation of Horizontal Ends of Connected Components

Intensity maps are formed from ultrasonic raw data. The decrease inusable ultrasonic raw data near the ends of boundaries will result in athinning of high intensity content. Using the Canny edge detectionprocess to determine the true boundary in these areas will result inpoor determination of the true OD/ID boundary as the edges output curvearound the high intensity content, where in fact the true boundary maybe flat. FIG. 43 illustrates the edge output from the Canny edgedetector 4302 in red. Indeed the curvature defined in the area 4304where the intensity tapers off is very high and does not do a good jobdefining the true boundary. A better way to define the boundary in areaswhere the intensity content tapers off is to use the curvature of themaximum intensity pixels (in the vertical dimension) to approximate thecurvature of the true boundary. FIG. 43 provides a good example wherethe maximum intensity pixels 4306 (in black) approximate the shape ofthe true boundary. The true boundary in this case is a flat plate.

The curvature of the boundary near the end of detected edges is thusapproximated by the curvature of the maximum intensity pixels in thevertical strips of the intensity map containing both the detected edgesand pixels of maximum intensity. This approximation is performed onlyif, when dilated, the pixels of maximum intensity are all connected toeach other (i.e. they belong to the same connected component). Thecoordinates of the maximum intensity pixels are then translatedvertically such that the pixel of maximum intensity near the side of thedetected edge is connected horizontally adjacent to it.

Boundary Interpolation

In areas between the definitions of the true boundary, where no trueboundary edges are defined, the true boundary can be interpolated suchthat true boundary edges are connected together. This is the last stepof the boundary definition process. FIG. 44 illustrates how the boundarycan be interpolated. Areas between the boundary segments 4402 extractedfrom the edge detection process are connected with straight lines 4404.

The sequential operation of an example algorithm for boundaryrecognition and definition is shown in the flow chart of FIG. 45. Theintensity map 4502 is subjected to a series of image processingalgorithms, including edge detection 4504, dilation of the edge 4506,thinning of the dilated edge 4508, trimming of erroneous pixels 4510,edge junction removal 4512, removal of bottom portions of connectedcomponents 4514, removal of small connected components 4516, andhorizontal end approximation 4518, finally outputting a total boundary4520 that includes the calculated and interpolated result of theprevious operations.

A comprehensive flow chart showing the entire boundary recognition dataflow is given in FIG. 69. Detailed descriptions of boundary recognitionparameters used in an example embodiment of the system are set out inTable A4 at the end of the Description. Detailed descriptions ofboundary recognition functions used in an example embodiment of thesystem are set out in Table A5 at the end of the Description.

Interior Focus Method (IFM)

The Interior Focus Method (IFM), like the Shifting Aperture Focus Method(SFM), is an algorithm whose purpose is to output an intensity map (animage where intensities are assigned to inspection coordinates ofinterest) given full matrix capture raw data and a smooth approximationof the OD boundary surface.

Once the OD intensity map has been determined and an OD surface modeldetermined from the high-intensity OD regions 32, Fermat's Principle canbe applied to model details of the inner pipe surface as well. Fermat'sPrinciple is one of the tools by which total focusing is extended beyondthe media interface (e.g. curve K 10 in FIG. 10), a technique which willbe referred to as the Interior Focusing Method (IFM).

Theorem 1 (the Modern Version of Fermat's Principle) states that thepath that sound takes from a point source emitter at point p, to anotherpoint q, is such that the time taken to traverse the path is astationary value (i.e. a minimum, maximum, or inflection point).

Extending the intensity equation shown above (Equation 2) to the areabeyond a media interface involves substituting the times t in theequation to those equalling travel path times whose paths experiencerefraction at the media interface. Fermat's Principle (Theorem 1) can beapplied in solving for these times.

Since K is piecewise-smooth, T_(ir)(K) has a finite number of stationaryvalues. These stationary values are denoted as t_(ir) ^(K′), t_(ir)^(K″), t_(ir) ^(K′″), . . . and the set of these stationary values asT_(ir) ^(K) where:

T _(ir) ^(K) ={t _(ir) ^(K′) ,t _(ir) ^(K″) ,t _(ir) ^(K′″), . . .}  (Equation 4)

Sets T_(ir) ^(K) and T_(jr) ^(K) are defined where i denotes element i210 with position vector e_((i)) and j denotes element j 212 withposition vector e_(j). The set of summations of all possiblecombinations between elements of T_(ir) ^(K) and elements of T_(jr) ^(K)are defined as T_(ij) ^(K)(r) where:

T _(ir) ^(K)(r)={t _(ir) +t _(jr) |t _(ir) εT _(ir) ^(K) ,t _(jr) εT_(jr) ^(K)}  (Equation 5)

By Fermat's Principle (Theorem 1), the above set contains travel pathtimes which can be substituted in Equation 2, to extend the totalfocusing method to imaging areas beyond the interface 10 between media6, 8. If the transmitter and receiver pair i 210 and j 212 are in thefirst medium 6, r is in the second medium 8, and K 10 is the curve whichdivides the first medium 6 and the second medium 8, the intensity of theimage at r can be written:

$\begin{matrix}{{I\left( {r,a} \right)} = {{\sum_{i,{j \in a}}{\sum_{t^{\prime} \in {T_{ij}^{\kappa}{(r)}}}{g_{{(i)}j}\left( t^{\prime} \right)}}}}} & \left( {{Equation}\mspace{14mu} 6} \right)\end{matrix}$

These results can be extended to three dimensional interfaces, asFermat's Principle holds in three dimensions. Furthermore, the resultsmay be extended for focusing through multiple media interfaces; n mediainterfaces will imply sound paths that will traverse n+1 media. In thiscase Fermat's Principle can again be utilized to solve for the truetravel paths. Some embodiments may employ these techniques to performmore complex analysis, such as scanning volumes containing more than twomedia or scanning in three dimensions at once rather than in a plane.

IFM is applied to create an intensity map of the ID scanning plane, suchas the ID intensity map shown in FIG. 13. After the ID intensity map 52has been created, the steps of boundary recognition 1720 and boundarydefinition 1722 are applied using the same algorithms described above tocreate the ID boundary definition 1724.

Implementation of the IFM subroutine can become very computationallyintensive if there are many coordinates for which correspondingintensities are evaluated. This is similar to the case forimplementation of the SFM subroutine, the difference being the IFMroutine is even more computationally taxing. The approach used to reducethe number of necessary computations in the IFM may be identical to thestrategy employed in the SFM routine. Limiting the number of coordinatesunder consideration, while focusing at the appropriate density to meetinspection specifications, imposes the following focusing strategy.First, intensities of coordinates on a course grid may be calculated.The strategy that may be employed is to first calculate intensities ofcoordinates on a course grid.

At this point, depending on user defined parameters, the IFM subroutinemay end, outputting the intensity map to the boundary detectionsubroutine, or proceed to define new coordinates for which to assignintensities. If the latter course is taken, newly defined coordinateswill be positioned around coordinates with high intensities alreadyassigned to them. The cutoff intensity for coordinates of which newlydefined coordinates focus around is defined in some embodiments by thevector zoomPercentage (see Table A10). Once the new coordinates havebeen defined, again depending on user defined parameters, the SFMsubroutine may exits, or may proceed to further focus around coordinatesof high intensity. The process of identifying high intensity coordinatesand then refocusing around them can be executed an arbitrary number oftimes, and may be specified by the user.

One cycle of the refocusing process is illustrated in FIG. 19.Coordinates spaced coarsely apart are represented as either white orblack circles. The white circles represent those coordinates whoserespective intensities are below the cutoff intensity for which newcoordinates are defined. Conversely, the black circles represent thosecoordinates whose respective intensities exceed the cutoff intensity forwhich new coordinates are defined. The gray circles represent newlydefined coordinates around high intensity coordinates. If the graycircles are defined on iteration i+1 of the coordinate definition andintensity assigning process, dx(i)/dx(i+1)=dz(i)/dz(i+1)=4 for theexample illustrated by FIG. 19.

FIG. 71 provides a full data flow for the IFM process. Detaileddescriptions of ID imaging parameters used in an example embodiment ofthe system are set out in Table A10 at the end of the Description.Detailed descriptions of ID imaging functions used in an exampleembodiment of the system are set out in Table A11 at the end of theDescription.

Boundary Definition and Smoothing

The boundary output from the boundary recognition procedure describedabove may be susceptible to noise. Two main factors contribute to thisnoise. The first is due to the imaging of aberrations such as grating inthe intensity map calculated via SFM. The second is due to thequantization of the intensity map grid. Errors (noise) can arise in adefinition of a quantity due to a quantization of its state space.

To minimize the effects of noise in determining true boundary (either ODor ID) coordinates, standard filtering techniques may be employed. Theboundary outputted from the boundary recognition algorithm may in someembodiments be filtered via two successive processes. The first filterused to eliminate noise from the input data may be a median filter ofvariable window size. This has the effect of removing high frequencynoise from the input coordinates. The next filter used may be aSavitzky-Golay filter. This filter reduces quantization noise byapproximating the input coordinates by an unweighted linearleast-squares fit using a polynomial of a given degree. Utilization ofpolynomials of higher degrees makes it possible to smooth heavily whileretaining data features of interest.

To initially eliminate noise from the calculated boundary, a medianfilter may be employed. Median filters implement a sliding window to asequential data sequence, replacing the center value in the window withthe median value of all the points within the window. Such a medianfilter may be chosen for its combination of good noise eliminationcoupled with its edge-preserving features. For both the OD algorithmsand the ID algorithms, the window width of the algorithm may beconfigured by a user in some embodiments. In some embodiments, thewindow width may be set to 5 mm by default.

Savitzky-Golay smoothing filters may also be used due to their superiorperformance in smoothing out a noisy signal whose frequency range(without noise) is large, as the corroded regions under a pipe weld capmay be seen to be with their potentially sharp edges. Savitsky-Golayfilters perform a least-squares fit of a windowed set of consecutivedata points to a polynomial and take the calculated central point of thefitted polynomial curve as the new smoothed data point. A set ofconvolution integers can be derived and used as weighting coefficientsfor the smoothing operation, a computationally-efficient process. Thismethodology is exactly equivalent to fitting the data to a specifiedpolynomial. The smoothed data point (y_(k))_(s) by the Savitsky-Golayalgorithm is

$\left( y_{k} \right)_{s} = \frac{\sum\limits_{i = {- n}}^{n}{A_{i}y_{k + i}}}{\sum\limits_{i = {- n}}^{n}A_{i}}$

where A_(i) are the convolution integers, and the data window goes from−n to n.

In some embodiments, the user-adjustable parameters for theSavitsky-Golay filters used in the OD/ID smoothing are:

-   -   1. Smooth Width: The smoothing window width expressed in mm. The        algorithm uses the appropriate “X Spacing” parameter to        determine the window width in elements:

${Number}_{elements} = \frac{{Smooth}\mspace{14mu} {Width}}{X\mspace{14mu} {Spacing}}$

-   -   2. Smooth Order: The polynomial order to be used in the        Savitsky-Golay algorithm. It is generally smaller than the        smoothing window number of elements. A typical value for Smooth        Order is 4.

An example data flow for boundary definition by the above processes isshown in FIG. 70. Detailed descriptions of boundary definitionparameters used in an example embodiment of the system are set out inTable A6 at the end of the Description. Detailed descriptions ofboundary definition functions used in an example embodiment of thesystem are set out in Table A7 at the end of the Description.

IFM Outer Diameter (OD) Boundary Preparation

The interior focus method (IFM) calculates travel times from probe arrayelements to points under the outer diameter via Fermat's Principle. Dueto a change in the refractive index at the OD, refraction of ultrasonicwaves will occur at this interface. Very small, high frequencyvariations in the definition of the OD surface will invite a largenumber of travel path solutions between probe array elements andinspection points under the OD, intersecting small regions in thedefined OD surface. These travel path solutions are due to either truehigh frequency variations in the OD surface, high frequency noiseintroduced into the OD definition, or a combination of both. The signalprocessing techniques that are described below serve to both toeliminate erroneous travel time solutions caused by high frequency noisecontent in the input OD boundary and to reduce IFM computation time.

While smoothing the defined OD surface can eliminate spurious travelpath solutions due to high frequency noise, it is not obvious whateffect this will have on travel path solutions induced by true highfrequency variations in the OD surface. Eliminating high frequencycontent of the defined OD surface may reduce the number of travel pathswith solutions very close to each other, but will generally preserveeffectively distinct solutions. For example, a series of travel pathsolutions 4.52 μs, 4.55 μs, 4.48 μs, 6.21 μs, 6.18 μs, 6.23 μs couldfeasibly be reduced to 4.53 μs and 6.22 μs. Programmatically, utilizingthe latter travel time solutions may produce similar intensity mapsoutput out of the IFM subroutine as those that can be produced utilizingthe former travel time solutions, since travel times of interest areretained. Features of interest in the intensity map will be preserved,with the added benefit of reduced computation time of the IFMsubroutine, due to a fewer number of computations involving travel timesolutions being executed.

Thus, the smoothing operations may serve to eliminate the effects ofnoise with a beneficial side effect. Smoothing of the OD boundary servesfirstly to eliminate erroneous travel time solutions due to highfrequency noise, while reducing the number of travel time solutions tothose that are effectively distinct. The smoothing methods employed onthe boundary of the OD may include a median filter followed by aSavitzky-Golay filter. They may be the same filters used for the purposeof boundary definition. These filters have the effect of eliminatinghigh frequency content while preserving data features of interest. Sincethe utilization of these filters for preparation of the OD boundary asinput into IFM has objectives different than the utilization forboundary definition, filtering parameters used may be different. Largerwindow sizes may be used for the Savitzky-Golay filter in IFM ODboundary preparation than those used in OD boundary preparation, sincepreservation of high frequency content in the OD boundary is notdesired.

Finally, the smoothed data may be downsampled. This has the primaryeffect of reducing computation time necessary to calculate travel timesvia the IFM subroutine, while retaining travel time solutions ofinterest.

An example data flow for IFM OD boundary preparation by the aboveprocesses is shown in FIG. 67. Detailed descriptions of IFM OD boundarypreparation parameters used in an example embodiment of the system areset out in Table A8 at the end of the Description. Detailed descriptionsof IFM OD boundary preparation functions used in an example embodimentof the system are set out in Table A9 at the end of the Description.

Adjustable Mirror

Some example embodiments may be able to adjust the angle of projectionof the ultrasound probe array 200 relative to the surface being scanned.For example, when scanning a pipe wall, some embodiments of themanipulator 100 may make use of an adjustable mirror 154 that reflectsultrasound projected from and reflected to the probe array 200. FIGS. 46and 47 show an example embodiment of the carrier 102 having anadjustable mirror 154. The ultrasound probe array 200 projectsultrasound from a linear array of elements arranged on its active face152. Ultrasound waves are projected toward the mirror 154, whose anglecan be adjusted by a mirror adjustment assembly 156 including a motor.By projecting ultrasound from one or more elements and sensing with oneor more elements at different angles of reflection, it is possible tooptimize the angle of the mirror to ensure that the ultrasound waves arebeing projected normal to the pipe surface. In some embodiments, thisoptimizing procedure may be performed prior to each frame of FMC datacollection. The mirror adjustment assembly 156 may be controlled by theexternal controller via the probe data connectors 108.

In embodiments having a mirror 154, the transducer is positioned to firetangentially instead of radially to the inspection surface. The mirrorplacement is designed to optimize the range of alignment accommodationwhile minimizing the added UT beam transit distance to the inspectionsurface.

The manipulator 100 may also be controlled by software to adapt thechange in inspection step size given possible changes in mirror angle.The control of the mirror may be achieved in some such embodiments bypulsing a group of elements 202 and summing their response. Theamplitude of the response is monitored after a series pre-defined stepincrements. Should the response level drop below a user definedthreshold, the module controlling the manipulator 100 will pulse theuser defined control aperture while driving the mirror 154 through adefined range of angles at a defined step size. The system sums theresponses at each step and then drives the mirror to the anglecorresponding to the peak amplitude.

The user interface used in such an embodiment may have a GUI where allrelevant control variables are entered. The user may have the capabilityto change the tracking parameters during a data acquisition scan as toeffect an optimal control region on the inspection surface.

Further details on optimizing the scanning angle using the adjustablemirror are provided below in the description of the data acquisitionprocedure as applied to pipe weld inspection.

Application to Pipe Weld Inspection

Once the OD and ID surface profiles have been modeled, they can be usedto determine structural characteristics of the pipe wall or otherscanned object. For example, the OD profile 36 and ID profile 38 for asingle transmit-receive cycle are combined in the graph shown in FIG.14. By juxtaposing these two profiles, information about the thinnestpoint 40 in the scanned portion of the pipe wall can be assessed andmade visible to a user.

Thinning of feeder pipe material adjacent to and within the weld area isa concern for power generating stations and other industries Thinningmay be found around the entire circumference of the joint with somehighly localized areas directly under the weld cap. The location ofthese areas may not be predictable due to the association with the weldroot condition. FIG. 48 shows an example of a pipe wall exhibitingthinning.

A procedure is described below which provides a method and requirementsfor performing thickness measurements on heat transport feeder pipingwelds at nuclear power generating stations, using a Micropulse™ultrasonic inspection and data acquisition system. This exampleembodiment is illustrative, and the methods and devices described may beimplemented in various embodiments and applied various contexts.

The inspection procedure described below is intended to detect localizedand broadly based wall thinning in the regions adjacent tocircumferential welded joints in feeder piping. This procedure appliesto various pipe sizes.

As shown in FIG. 49, the region of inspection is 20 mm on either side ofthe weld centre line. However, geometric limitations may constrain theability to achieve this coverage. This inspection records all ultrasonicsignals transmitted from each element to all possible receivingelements. Each element in the array is fired in sequence. Thus, for a128 element linear array, each of the 128 elements that has fired has128 receptions for a total of 16,384 separate A-scans. This process isrepeated at each location as specified in the inspection sequence.

The data recorded is then submitted for analysis. The data analysissoftware produces OD and ID profiles of each inspection data set. Thedata sets are acquired at discrete circumferential locations. Togetherthe series of profiles around the feeder constitutes the results of theinspection. A separate table records the minimum distance between the ODand ID profiles for each circumferential location, the axial location ofthe minimum in the slice and the circumferential position of the slice.

Special circumstances and exceptions to the procedure may exist. Forexample, hot spots or high local fields may create a situation whereoperators cannot access the work area. In addition, in the case ofGrayloc™ fitting to pipe welds the inspection coverage may be modifiedto collect a zone spanning 10 mm on the Grayloc™ side of the weld centreline to 20 mm on the fitting side of the weld centre line, as shown inFIG. 50. (In the context of this embodiment, a Grayloc™ fitting refersto a conical pipe fitting with its narrow end matching the diameter of astraight pipe section.)

DEFINITIONS

The following definitions are used within the context of pipe weldinspection as described below.

FMC: Full Matrix Capture. Ultrasonic data collection strategy in whicheach element in the transducer is individually pulsed while all elementsreceive. This is repeated for each element in the transducer until allelements have been fired. This strategy creates a data array of n by nwhere n is the number of elements in the transducer. As a consequencethe data files for a FMC inspection is significantly larger than for theequivalent conventional (e.g. phased-array) technique at the sameresolution.

Home Position: Circumferential location of the inner rotating ring suchthat the manipulator may be safely unlocked from its closed position.Home position is indicated in both the VIM and the software asidentified by the Hall sensor on the manipulator.

Main diagonal: A group of send-receive elements in the data collectedusing FMC where each transmitting element is its own receiver. The maindiagonal view of the FMC data set is identical to the conventionallinear electronic B scan. The main diagonal view is the default view ofthe FMC data B scan.

Matrix: Data structure created when using the FMC data collectionstrategy. If the columns of the matrix are assigned to identify thetransmitting element, then the rows of the matrix correspond to thereceiving elements. Each element of the array then corresponds to an Ascan related to that transmitter receiver pair. For example: acombination of transmitting on element 17, receiving on element 32 wouldproduce an A scan that would be located under the 17th column on the32nd row of the FMC data matrix.

Start Position: Circumferential location, with respect to themanipulator, where the scan is initiated. The start location maycorrespond to the home location or can be offset from the home location.

TFM: Total Focus Method. Generic name for a variety of automated dataanalysis strategies that use the data created via the FMC method. TFMrelies on summing up the amplitude values in a range of time indices inA scans from various transmitter-receiver combinations. Where validsurfaces exist, the amplitudes constructively interfere to image thesurface. Where no such surface exists, the amplitudes destructivelyinterfere forming no image. TFM is also described as being equivalent tofocused phased array throughout the entire inspection volume.

Abbreviations and Acronyms

The following abbreviations and acronyms may be used within the contextof pipe weld inspection as described below.

A Scan Time-Amplitude plot for a specific Tx-Rx pair B Scan Collectionof A scans across the inspection array, in this case transverse to theweld, where amplitude is represented in gray or colour scale DPDigitization Point - point along the time axis of the A scan feeder Pipecarrying heavy water coolant to or from the individual fuel channels FMCFull Matrix Capture Inspection Array Multi element transducer used forFMC data collection ID Inside Diameter NEOVISION Custom UT dataacquisition and analysis application for FMC data sets NPS Nominal PipeSize OD Outside Diameter Tmin Minimum wall thickness TFM Total FocusMethod TSSA Technical Standards & Safety Authority UT Ultrasonic TestingWPIT Weld Profile Inspection Tool

Data Acquisition

FMC inspection superimposes a probe trajectory of a cylindrical geometryon the weld configuration. Depending upon the nature of the joint andthe placement of the manipulator over the joint, some distortion of theOD and ID signals can occur. Areas where this may occur are the cheekareas of straight to bend geometries or Grayloc™ to bend geometries. Apossible remedy for this is to attempt re-positioning the manipulatorover the joint with the intent of optimizing the signals in the regionswhere distortion is experienced.

There are two types of obstructions that limit inspection of welds. Thefirst type is adjacent structures which block installation of the toolin the desired inspection area. In this event the inspection area may beinaccessible. A second type of obstruction exists where the manipulatoris prevented from achieving full rotation due to intrusion into themanipulator path by adjacent structures. The data acquisition softwaremay have an option where, should an obstruction be detected, themanipulator will drive to the scan origin and then acquire data in theopposite direction until either full coverage is obtained or themanipulator again contacts the obstruction. In the latter case theportion of the circumference where no data has been obtained may benoted as access restricted.

FIG. 51 shows an example configuration of the various data systems usedin collecting and processing the inspection data. The inspection systemas a whole is divided into two separate spheres; the data acquisitionaspect and the data analysis aspect. The UT (Ultrasound Testing) datagenerated during data acquisition in the vault 5002 is collected,reviewed and recorded at the acquisition site 5004. After the recordingprocess, the data is exported to the analysis site 5006 where theanalysis blade system 5008 resides. The export process will take a fewminutes (2-3) due to the file size and the network speed.

Analysts may be able to remotely access the analysis site 5006 such thatdata need not be transferred to the analyst machines 5010. The analystsevaluate the scan data to set processing parameters to appropriatevalues prior to submitting a job. Once the job has been processed, theanalysis operator will be able to review the profiles and associatedintensity maps.

A simplified diagram of the equipment configuration in the vault 5002 isgiven in FIG. 52.

A diagram of the equipment configuration at the trailer/remoteacquisition site 5004 is given in FIG. 53. If 12 fibre MT has not beeninstalled between the acquisition site 5004 and the acquisition hardwarein the vault 5002, this fibre may be run to the from the acquisitionsite 5004 to the penetration.

The following is a list of equipment used for data acquisition underthis example:

Quantity Item Description 3 Micropulse ™ 5PA Data AcquisitionInstrument - 128 channels with Gigabit Ethernet interface, Firmwareversion P1 4.5.1.29 and P2 4.5.0.10 or latest qualified release 3 VaultInterface Module Interface unit for manipulators, Micropulse ™ andperipherals 2 Acquisition computer 4 GByte memory, Core2 Duo 23 GHz orabove, 32 bit Windows XP, 500 GByte HDD, Gigabit Ethernet interface,Dual 19″ monitors 3 Neovision ™ software Data acquisition software 1.0R0 or most recent qualified release 3 Manipulator 2″ 3 Manipulator 2.5″3 sets Mounting Brackets for 0 degree configuration 3 sets MountingBrackets for 6 Provided by Kinectrics ™ degree configuration 3 ArrayTransducer Custom 128 elements, 7.5 MHz, 0.27 mm pitch, with 8 m cableand Hypertronix ™ connection 3 Temperature sensor 2 Reference specimen2″ 0 degree configuration 2 Reference specimen 2.5″ 2 Reference specimen2″ 6 degree configuration 4 Reference Block stand 12 fibre with MTconnections for station side Fibre Optic Cables and vault side (lengthsrequired vary depending upon configuration) 2 Network Switch Shouldsupport Gigabit standard 3 CAT 5e or CAT 6 Use of CAT 5 cable is notgenerally Ethernet cable recommended 1 Ethernet addressable power bar AsProgrammable headsets Communications sets to be set to a dedicatedrequired channel 1 Couplant Pump Submersible type with tubing 1 Watercontainer Large capacity - 60 litre suggested 1 Flow Control Module AsTrailer/Remote vault As per current practice required camera monitor AsVault camera control As per current practice required unit AsContainment side camera As per current practice required controller unitAs PTZ camera As per current practice required 1 Penetration insertUniversal or SG penetration (should support MTP fibre) 1 Stick Light 2GFCI 15 Amp Fusible Link to U-ground As Adapter Twist lock to U groundrequired

Demineralised water for use as couplant is generally allowed to sit fora minimum of 48 hours, preferably 60 hours prior to use. This is topermit gases dissolved in the water to condense out. Heating the waterto 50 C and then allowing it to cool will accelerate this process. Ifthe water is not allowed a period to de-aerate it is very likely bubbleswill nucleate on both the feeder and transducer surfaces. The bubbleswill continue to grow and may impair the quality of the scan data.Swabbing a thin layer of gel couplant on the probe face may reduce thetendency for bubbles to form, however the couplant will dissolve in timewith exposure to water.

As an alternate to having the water sit for an extended duration anothermethod is available. Effective de-aeration may be performed by sprayingthe water into a suitable container using a common garden nozzle. Thenozzle should be set to a fine pattern. The pressure drop at the nozzlecauses the air to separate from the water. Properly done, the watershould be ready for immediate use.

A catenary cable containing breathing air hose(s), 120 VAC power andcommunication fibre may be prepared and tied off at the platform withthe other end tied off at a suitable point on the vault floor. The fibremay be connected to the penetration, the air hose(s) to a dedicatedbreathing air header, and the 120 VAC to a GFI and designatedreceptacle.

On the platform the fibre may be terminated at the switch and the powerat the addressable power bar. The platform equipment may be mounted on asuitable cart or common instrument rack.

Instrument Calibration

Conventional UT data collection instruments may benefit from periodiccalibration when employing techniques that utilize signal amplitude as ameans to detect, discriminate and size indications. Conventional normalbeam techniques as applied for feeder thickness measurement rely uponthe accuracy and stability of the instrument time base. The time base ofdigital instrumentation is dependent upon the system clock, devices thatare known for their long term stability. As such there may be no need toperform annual calibration of the instrument since signal amplitude isnot a factor. The embodiments discussed in this application similarly donot rely upon signal amplitude for measurement; therefore periodicinstrument calibration is not mandatory.

Calibration may be advisable when significant differences are noted whenconnecting a transducer to two separate instruments given the sameset-up parameters. The data collection instrument may be calibrated atsuch times when replacing components, upgrading hardware or repairs tothe instrument are necessary.

Transducer Characterization (Maintenance)

Transducers may be characterized upon receipt from the manufacturer orwhen first placed into service. Transducers may then be characterized atyearly intervals following being placed into service. Thecharacterization activities may be conducted by qualified maintenancetechnicians. The characterization tasks are considered to be part of thesystem maintenance activities and are not part of the calibrationactivities during the inspection. The manufacturer's report for thetransducer may be referenced during the characterization. Correctprocessing of inspection data depends upon accurate measurements ofprobe parameters. These parameters include:

(1) Element Functionality Check

The NEOVISION™ application has a feature which introduces a series oflinearly varying delays into each channel that when viewed appear like asaw tooth pattern. This feature is the Probe Element Test and is foundunder the UT Calibration tab. A quick check of the main diagonal B scanresponse will highlight any missing elements. The operator may use thecursors to identify the channel number of the missing element(s). Thechannel numbers of any missing element(s) may be recorded in themaintenance record.

(2) Transducer Delay

The transducer can be evaluated on the reference block; however, slightmisalignment can introduce errors in the delay measurement. Given theclearance dimensions in the various components of the manipulator, it isdifficult to ensure precise location of the probe with respect to thereference block surface. For the purposes of this inspection, thetransducer delay is measured separately on a metrology fixture. Thisvalue is recorded and is subsequently confirmed to be within anacceptable range once the transducer is mounted in the manipulator andmeasured on the reference block.

(3) Element Delay

In addition to the overall transducer delay, the delay of each elementmay also be evaluated. Transducer elements have been found to vary indelay significantly across the face of the transducer, in some cases upto ½ cycle. Significant variation will introduce error into thesubsequent processing of inspection data.

(4) Frequency Spectra Test

The frequency spectra test acquires the individual responses of theelements from a defined target and then performs the Fourier transformon a segment of the A-scan. The resulting frequency spectrum is comparedto the original as provided in the manufacturers report. This value isrecorded and is subsequently confirmed to be within an acceptable rangeonce the transducer is mounted in the manipulator 100 and measured onthe reference block during maintenance.

(5) Pulse Duration Test

The pulse duration test is associated with the frequency spectra test.This particular test involves measuring the duration of the reflectedwaveform at both 6 dB and 20 dB drop points. This value is recorded andis subsequently confirmed to be within an acceptable range once thetransducer is mounted in the manipulator 100 and measured on thereference block during maintenance in the rubber area.

(6) Beam Orientation

This test verifies the direction (angle) of the emitted beam withrespect to the passive and active planes of the transducer. The beam isgenerally to be within 0.5 degrees of the vector normal to thetransducer surface. Transducers found to be outside of thisspecification may not be suitable for FMC inspection.

Additional Tests (Maintenance)

This series of tests and steps are not associated with the transduceralone; however, they do affect subsequent data analysis processes. It isintended these checks be performed at a maintenance facility or in aproperly equipped rubber area.

(1) Water DAC Curve

The water DAC (Distance Amplitude Curve) is created by scanning over thewater column steps of the reference tube while the DAC curve isinactive. The gain is adjusted such that the interface echo does notexceed 80% FSH (Full Screen Height) on any of the steps. The data fileis saved and then analyzed.

The maintenance technician using the cursors on the A-scan records thetime of arrival and peak amplitude of the interface signal. The stepsare measured in series from nearest to furthest. The amount of gainneeded to raise the interface signal to 80% FSH is calculated andentered along with the time of arrival of the peak in the DAC table.

A series of points is created and a best fit curve is plotted throughthe data series. Any significant outliers to the curve may beinvestigated for anomalies (potential air bubbles). See FIG. 54 for anexample of a DAC curve. The Water DAC is then activated and a final scanof the reference tube is conducted to ensure all interface signals areat 80% FSH regardless of water column distance. If there is anydeviation beyond 1.25 dB (+/−5% FSH) the curve should generally berecalculated.

The Water DAC curve in all other acquisition computers may be updatedwith the most recent curve. Note that while the Water DAC curve isunique to each transducer, any DAC curve demonstrated to provide theappropriate responses for other transducers may be applied.

(2) Transducer Gain Trim

Elements within a transducer can vary in sensitivity +/−4 dB or morefrom each other. Variation in instrumentation, cables and connectors canalso compound the element to element sensitivity variation. Theconsistency of the TFM result may be improved when these variations arecorrected.

The following test may be performed at a static position rather than aspart of the calibration scan. The acquisition operator may use thecalibration feature in the NEOVISION™ application to adjust and confirmthe overall gain and the gain of the individual channels. Thecalibration feature is found under the UT Calibration tab in Set-upmode.

(a) The Acquisition Operator then prepares the gain trim calibration bysetting the calibration gate to encompass the interface signal. TheAcquisition Operator ensures there are no bubbles on either thetransducer face or on the reference tube as these will invalidate thegain trim adjustment.

(b) The Acquisition Operator starts the calibration. NEOVISION™ willautomatically adjust the gain and then after a short period(approximately 5-10 seconds) return a message indicating the range ofgain needed to adjust the individual elements. In some cases the gaintrim utility will report error(s) if elements are outside the nominalgain trim range. This may be the case for missing/dead elements and/orconditions where air bubbles exist either on the transducer/mirror orreference tube surface. The Acquisition Operator may investigate theerror messages to correct any conditions beyond the expected result.

(c) The Acquisition Operator may evaluate the reported range to compareit to previous gain trim adjustment.

(d) The Acquisition Operator should generally note any differences inindividual element response from the previous adjustment. The gain trimadjustment may in some embodiments be within 2.0 dB of the previousevaluation for the same transducer. Differences may arise from thepresence of very small air bubbles or transducer aging. The differencesmay be resolved before proceeding. If the gain trim is within acceptablelimits the Acquisition Operator may update the Probe File and apply thegain trim settings for the current inspection. This value is recordedand is subsequently confirmed to be within an acceptable range once thetransducer is mounted in the manipulator and measured on the referenceblock.

(3) Metal DAC Curve

The metal DAC is created by scanning over the metal path steps whileusing the Water DAC but no secondary DAC. The gain is adjusted from thebaseline gain of the Water DAC such that the first backwall echo doesnot exceed 40% FSH. This the difference between the Water DAC baselinegain and the gain required to being the first backwall reflection to 40%is the gain offset for the Metal DAC. The file is then saved andanalyzed.

The maintenance technician using the cursors on the A-scan generallyrecords the time of arrival and peak amplitude of the first backwallsignal. The steps are measured in series from thinnest to thickest. Theamount of gain needed to raise the backwall signal to 40% FSH iscalculated and entered along with the time of arrival of the peak in theDAC table.

A series of points is created and a best fit curve is plotted throughthe data series. Any significant outliers to the curve may beinvestigated for anomalies. The Metal DAC is then activated and a finalscan of the reference tube is conducted to ensure the all interfacesignals are at 40% FSH regardless of metal thickness. In someembodiments, if there is any deviation beyond 1.25 dB (+/−5% FSH) thecurve should generally be recalculated.

The metal DAC curve is saved. The Metal DAC curve in all otheracquisition computers may be updated with the most recent curve. Notethat while the Metal DAC curve is unique to each transducer, any DACcurve demonstrated to provide the appropriate responses for othertransducers may be applied.

(4) Temperature Transducer Check

The temperature transducer is checked for functionality and is not partof the transducer characterization. This can be conducted by measuringthe temperatures in an ice water bath as well as boiling water.Alternatively calometric devices may be used to test and calibrate thetemperature transducers. Although the temperature transducer iscalibrated for a limited range, repeatable demonstration of the rangewill indicate the transducer is operating normally.

After calibrations defined in the previous paragraphs are performed, theresulting updated transducer file may be transferred to the Acquisitioncomputer. The new file may be used when the tool containing the probe(e.g. the manipulator 100) is applied.

System Calibration

System calibration is defined as verifying the value for operatingvariables and confirmation of the continued performance of the FMC dataacquisition system as a whole. The process of converting the UT datainto the final result is time-consuming, thus analysis is not part ofthe calibration procedure. Instead it is generally sufficient todemonstrate the relevant UT parameters have not deviated from therecommended values over the course of the inspection.

System calibration encompasses checking:

-   -   1. Encoder verification    -   2. Element Functionality Check    -   3. Transducer—manipulator Alignment/Probe Axis Function Check    -   4. Metal path measurement verification    -   5. Transducer delay check    -   6. Water path attenuation check    -   7. Temperature sensor verification    -   8. Water path measurement check

Should the calibration values fall outside of the prescribed or previousapplicable values, the calibration may have failed. The AnalysisOperator may address possible causes but not vary parameters (e.g.eliminate air bubbles but not change velocity) and re-attempt thecalibration scan. If the calibration results are acceptable theinspection can proceed. If the calibration has failed then allinspection files obtained following the most recent valid calibrationare generally discarded. The feeder welds in question may be rescanned.

Calibration Equipment

The calibration equipment consists of the following items:

1. 2″ reference block specimen (see FIG. 55)

2. 2″ reference block specimen for 6 degree configuration (see FIG. 56)

3. 2.5″ reference block specimen (see FIG. 57)

4. reference block stand

5. transducer/manipulator combination connected to UT instrument

The calibration equipment is configured with the manipulator 100installed on the reference block as depicted in FIG. 58, with thereference block specimen depending upon the manipulator size to becalibrated. The arrow 5802 in FIG. 58 indicates the approximate startpoint of the scan.

Calibration Method

The platform operator clasps the manipulator on the appropriate sizereference tube ensuring the manipulator outer ring is engaged in thestand locator pins. In some embodiments there is a separate referencetube for the conventional and 6 degree manipulator configurations.Calibration of each manipulator configuration is conducted on theappropriate reference tube.

The stabilizing arms are then latched over the manipulator. The platformoperator indicates to the acquisition operator when the system is readyfor the calibration verification. A flow chart for the sequence ofcalibration steps is given in FIG. 59.

The acquisition operator starts the couplant pump and the scan under theset-up window of NEOVISION™. The acquisition operator observes signalsin both A and B scan windows to confirm the manipulator is full ofcouplant. Should the manipulator not fill with couplant or be incapableof maintaining water column for the duration of the calibrationexercise, the cause may be identified and fixed before continuing withthe inspection.

1. Encoder Verification

Verification of the proper behaviour and values for the encoder iseasiest to perform by driving the manipulator from the home position onefull revolution to arrive back at the home position. These locations areeasily observed by lining up the parting line inner ring halves with theparting line of the stationary ring halves. The value should generallymatch the circumference to within +/−0.3 mm. In example embodiments witha 2″ manipulator and a 2.5″ manipulator, the distance is: 195 mm for the2″ manipulator and 245 mm for the 2½″ manipulator.

The following steps may be used by an operator:

-   -   (a) Approach the home position from the negative side by driving        in the positive direction.    -   (b) When the split in the inner rings align with the split in        the outer ring, set the position to 0.0 under the        ‘Motors/Relays’. If this position is overshot, the manipulator        should not be driven in the negative direction, as doing so may        build manipulator lash into the calibration. If the position is        overshot, repeat the approach from the negative side.    -   (c) From this zero position, drive the manipulator in the        forward direction, 195 mm for the 2″ manipulator, and 245 mm for        the 2.5″ manipulator.    -   (d) The manipulator should generally perform a 360 degree        rotation and stop so that the split in the inner rotating ring        aligns with those of the outer rings. Any offset from this        location may represent an error in the calibration or mechanical        operation of the manipulator.    -   (e) The test is conducted in the opposite direction by returning        the manipulator to the home position.

The final value in some embodiments should be 0 mm within +/−0.3 mm.

Failure to meet these requirements in either direction may be due toseveral possible causes: incorrect settings, electrical problems ormechanical problems. If the encoder is beyond these values in eitherdirection the manipulator may be removed from service for furtherevaluation.

Measurement of encoder accuracy can be affected by backlash in themanipulator drive train. The backlash varies from manipulator tomanipulator and can increase in time due to wear in the gear train andmanipulator bearing surfaces.

2. Element Functionality Check

The NEOVISION™ application has a feature which introduces a series oflinearly varying delays into each channel that when viewed appear like asaw tooth pattern. This test can be conducted in a static position whilein set-up mode on the reference sample. Alternatively this test may alsobe conducted as part of the calibration record by noting thepresence/absence of the element responses over a calibration feature onthe reference sample.

A quick check of the main diagonal B scan response will highlight anymissing elements. The operator may use the cursors to identify thechannel number of the missing element(s). The channel numbers of anymissing element(s) may be recorded in the maintenance record.

If the transducer and instrument combination fail to meet therequirement identified in the “Element Functionality” factor set out inTable 3 below, the transducer and/or instrument may be removed fromservice for repair.

The Acquisition Operator then loads the UT and scan settings for theVerification scan. See Tables 1 and 2 below. The Platform Operator mayensure proper cable routing for the calibration scan. When ready, theAcquisition Operator starts the calibration scan.

TABLE 1 Example UT Parameters for 0 degree calibration Parameter ValueComments Averaging  1 Active aperture 128 Pulser mode Echo TriggerDigitizing 100 MHz Used for measurement accuracy Frequency Zero OffsetSpecific to probe - nominally 32 DP Range 1250 Gate Start 245 DP GateLength 2500 DP For acquiring 17 mm water column and angle Gain ~32-35 dBFunction of probe and system Threshold ~13% FSH Adjusted as required topreclude triggering on Initial Pulse

TABLE 2 Example UT Parameters for 6 degree calibration Parameter ValueComments Averaging  1 Active aperture 128 Pulser mode Echo TriggerDigitizing 100 MHz Used for measurement accuracy Frequency Zero OffsetSpecific to probe - nominally 32 DP Range 1500 Gate Start 350 DP GateLength 3000 DP For acquiring 17 mm water column and angle Gain ~32-35 dBFunction of probe and system Threshold ~13% FSH Adjusted as required topreclude triggering on Initial Pulse

The Acquisition Operator may evaluate the calibration scan once completeas per the items listed in the table below.

3. Transducer—Manipulator Alignment Check

Transducer—manipulator alignment may be evaluated by scanning across the5 flat regions of the reference block (such as the example blocks shownin FIGS. 55-57). These regions in some embodiments are inclined withrespect to the reference block axis. When correctly set, the signalamplitude will rise from the 2 degree surface to the 1 degree surface,peak on the surface parallel to the transducer face and then fall to the1 degree and again to the 2 degree surfaces.

The Acquisition Operator may drive the manipulator from the homeposition forward around the block by a distance of 145 mm for an example2″ manipulator and 185 mm for an example 2.5″ manipulator. TheAcquisition Operator may then set the motion increment to 0.1 mm andincrement relative to the current position while observing the interfacesignal. The Operator may continue to increment the position until thesignal is observed to peak and then fall. At this point the Operator mayset the current position of the manipulator; to 149.1 for the 2″manipulator and to 189.6 mm for the 2.5″ manipulator. Driving in theopposite direction may result in manipulator backlash being incorporatedinto the settings.

If the peak is found to occur on other surfaces, the transducer andmanipulator may not be aligned and should be adjusted. The manipulatormay be removed from service to address this issue.

Once the peak is identified and the manipulator circumferential positionset as per manipulator size, the Acquisition Operator may drive themanipulator to a point beyond zero: −3 mm is suggested. This may berequired to remove lash from the manipulator drive train. TheAcquisition Operator may drive the manipulator back to the 0 position(not necessarily home position). The Acquisition Operator is then readyto perform the remaining steps of the calibration.

The Acquisition Operator should generally select the WPIT Verificationscan, not the WPIT Calibration scan. The Verification scan confirms thefunction of the DAC curve whilst the Calibration scan builds the DACcurve.

The verification scan runs a series of positions around the ReferenceBlock, the metal path series, followed by the water column series,followed by the angle verification and the angled surface.

This check may be varied or omitted in embodiments incorporating areflector or mirror 154 that dynamically changes the scanning angle ofthe transducer. For example, in some embodiments having a mirror 154,transducer—manipulator alignment is evaluated by scanning across thecontinuously variable region of the reference block. This region iseffectively a cam surface that passes through a range of angles. Thecontrol system will drive the mirror to an angle to optimize theinterface signal. The angles reported should correspond to the angle ofthe surface at the various points that are checked.

To perform this function manually, the Acquisition Operator may drivethe probe axis to peak the interface amplitude at various locationsalong the span of the continuous surface. The probe axis angle isreported and compared to the expected angle. The difference between thereported angle and the expected angle should not exceed +/−1 degree insome embodiments.

4. Metal Path Measurement Verification

The accuracy of the metal path measurement may be verified by checkingvarious steps in the reference block. In some embodiments, a 2 mm, 8 mm,and 14 mm step are checked. Measurements specific to each examplereference block shown in Figured 55-57 are found in Tables A12 to A15 atthe end of the Description.

(a) This measurement is conducted on the A scan window of the matrixmain diagonal channel selection corresponding to the linear electronic Bscan. Any channel from the B scan may be selected for this measurement.

(b) For this measurement the time axis is set to half path and thevelocity is set to carbon steel under the Display Options tab.

(c) The measurement is made by zooming in on the region of first twobackwall echoes in the A scan such that the individual data samples canbe distinguished.

(d) Using both pairs of cursors, the measurement is made by placing onecursor on the first rising zero cross of the first echo and the othercursor on the corresponding zero cross of the second echo. Themeasurement is the difference between metal path values of these twocursors. This value is directly obtained from the INFO button on the Ascan window margin.

(e) The values for these steps are recorded in the calibration record.

If the value(s) fall outside the acceptable range, the cause of thediscrepancy should be identified and remedied before the inspectioncontinues. All data obtained prior to identification of the discrepancymay no longer be considered valid.

5. Transducer Delay Check

Due to the difficulty in precisely locating the manipulator on thereference sample the transducer delay can vary merely due to positionalinaccuracy. Transducer delay is therefore generally performed only as anindication.

For this test, the interface signal and second interface multiple shouldbe present in the scan data. This can be achieved on either of the 2 mmor 5 mm water column steps.

The transducer delay is evaluated by subtracting the measured watercolumn distance from the design water column distance for that step. Thedelta will be the change in probe delay from that currently set. Themost accurate measurement will be obtained from the first minimum of therespective signals as the subsequent distortion increases the error inthe measurement. Note this distance is generally evaluated indigitization points instead of water velocity based half path.

The Acquisition Operator may use other steps or channels to confirm areading that appears to be outside expected limits.

6. Water Path Attenuation Check

The water path attenuation curve may be verified by checking theamplitude of the interface signal.

The calibration software (WPIT verification) used in some embodimentshas a feature that reports the range of amplitude height variation for agiven element on the various water column steps. The amplitude of theinterface signal should generally remain within 50 to 99% FSH for anymain diagonal channel on all steps of the reference block. Deviationfrom this amount in the absence of other possible factors (air bubbles)suggests the water path attenuation curve is not accurate.

If the reported level is outside the range for an individual element,the Acquisition Operator may perform a manual assessment using anothertransducer element response. The Acquisition Operator may set the cursorlevel in the A scan window to 80% FSH. The Acquisition operator mayselect the desired channel and examine the amplitude response from therange of water column steps. If the responses fall within 50 to 99% FSH,the attenuation check is generally acceptable. If deviation is found toexist on several elements of the transducer, or if the range ofdeviation is found to be excessive, resolve the issue.

7. Temperature Sensor Verification

Given the robustness of these type of devices it is usually sufficientto verify the temperature reading is both stable and within expectedbounds. Nominal bounds anticipated for inspection are generally 15 to 45degrees Celsius.

The Acquisition Operator may save the calibration scan and record theresults of the calibration in an FMC Feeder Calibration Record. Inpreparation for the supplementary scan the manipulator is driven to thehome position if not already there.

Any anomalies may be resolved by appropriate steps (e.g. componentreplacement, eliminating air bubbles), and re-calibrated beforeproceeding with inspection.

8. Water Path Measurement Check

A supplementary scan may be required to perform a water path measurementcheck. The scan generally runs from 60 mm to 100 mm on the Referenceblock in steps of 0.5 mm. This scan may be conducted for both the 0degree and 6 degree configurations, however only the 0 degree result maybe evaluated at this time.

In some embodiments, water path measurement is confirmed by checking themeasurement deltas between the 2, 5, 8, 11, 14 and 17 mm water columnsteps in the reference block. This measurement is conducted on the Ascan window of the matrix main diagonal channel selection correspondingto the linear electronic B scan. Any channel from the B scan may beselected for this measurement. Measurements specific to each referenceblock may be kept on record and used as a reference in the calibrationprocess.

For this measurement the time axis may be set to half path and thevelocity may be set to water under the Display Options tab.

The measurement is made by zooming in on the region of interface echoesin the A scan such that the individual data samples can bedistinguished. Using both pairs of cursors, the measurement is made byplacing one cursor on the first rising zero cross of the interface echoand then indexing to the image of the next step, placing the othercursor on the corresponding zero cross of the interface echo. Themeasurement is the difference between water path values of these twocursors.

This value is directly obtained from the INFO button on the A scanwindow margin. Subsequent water column step measurements are then madein the same manner.

Parameters & Values for Calibration

Table 3 below identifies the parameter to be measured and applicabletarget value to be obtained.

TABLE 3 List of example calibration criteria and values Factor ValueRange Comments Element Not applicable No more than 13 Reject transducerif greater Functionality missing, no more than than 13 or 3 adjacent 3adjacent missing elements Probe delay 32 DP 2 DP Note - when at 100 MHzsampling rate Water column 80% FSH 99%-50% FSH DAC Metal path Blockspecific, +/−0.03 mm measurement Refer to Tables A12 to A15 Transducer -0 +/−1 manipulator alignment Temperature Not applicable 15-45° C.Observed to be functioning sensor function and rational Encoder check 0to 195 (2″) or +/−0.3 mm Check for backlash or 245 (2.5″) excess play -replace manipulator if necessary Water column Block specific, +/−0.01 mmMeasure the difference measurement Refer to Tables between the steps ofthe A12 to A15 Reference Block

Calibration Record Contents

The FMC Feeder Calibration Record may generally contain the followinginformation:

-   -   Date and Time of calibration file    -   Name of the calibration file    -   Transducer serial number, manipulator serial number    -   Micropulse serial number, Vault Interface Module serial number    -   Reference tube serial number    -   Name of Acquisition operator performing calibration    -   Name of Platform Operator performing calibration    -   Data table recording the results of the calibration

Intervals

A calibration scan of the reference block may be conducted at thefollowing intervals:

-   -   start of a shift    -   4 hour intervals    -   change of acquisition operator

In addition to the above, calibration scans may be performed whenever atransducer, manipulator or instrument is changed. The calibration checkis a confirmation that the inspection system remains in calibration andis fully operational. The calibration check file may be recorded withthe inspection data.

Ultrasonic Equipment Settings

The ultrasonic equipment settings may be available in a pre-definedconfiguration (set-up file). The Inspection Supervisor responsible forthe initial set-up of the equipment for the inspection campaign mayensure the most recent version of all set-up files are loaded into theNEOVISION™ software.

Nominal values for the settings are given Table 4, below.

TABLE 4 Nominal values for Ultrasonic testing Parameter Essential ValueComments Probe Element Yes 0.195 mm Fixed per probe specificationSpacing Probe Element Yes 0.27 mm Fixed per probe specification PitchProbe Element Yes 5.0 mm Fixed per probe specification Elevation ProbeElement Yes 128 elements - no greater than 13 Fixed per probespecification Count elements non responsive with and no greater than 3elements adjacent Probe Centre Yes As measured, within +/−10% of Fixedper probe specification Frequency nominal Transducer Yes Elements shouldbe within +/−2.0 dB As determined via procedure element gain of previoustrim Zero Offset Yes Should be within +/−2DP of known As measured viaprobe delay, however current designs are characteristics ~32 DP @ 100MHz or 16 DP @ 50 MHz Pulse width Yes 40 to 60 ns Optimized totransducer Pulse Voltage Yes 70 V Exceeding 100 V will acceleratetransducer aging Delay Yes ~30 DP but not less than 20 DP Variable -Feeder and weld cap geometry influence the range of this variable RangeYes ~500 to 850 DP Function of thickness & water column rangeDigitization Yes 50 MHz Optimized for data Rate transmission ratesDigitization Yes 12 Bit Resolution Averaging Yes 1 Data Collection YesFMC Mode Active Aperture Yes 128 Gain Yes Variable - nominal values in arange See procedure for details of 40 to 47 dB Filter Yes None Using thefilter influences subsequent processing Water DAC Yes On - as determinedby procedure Metal DAC No Off As per Neovision ™ 1.0 analysis softwareInterface Gate Yes On Set interface to Yes Off (unchecked) ZeroInterface Gate Yes ~225-350 DP but not less than 200 Function of scantype, i.e., 0 Start DP degree, 6 degree in case of 2″ Interface Gate Yes~1000 DP Function of feeder geometry Range Interface Gate Yes ~11% FSHAmplitude Prem-trigger Yes On - 30 DP acquisition Gate 1 No Off Inputfor data quality indicator Gate 1 Start No Input for data qualityindicator Gate 1 length No Input for data quality indicator Gate 1 No 5%FSH Input for data quality Amplitude indicator Outer Diameter No 18Input for data quality Aperture indicator Minimum No 50% Input for dataquality Percentage indicator Preferred No 80% Input for data qualityPercentage indicator Inner Diameter No 32 Input for data qualityAperture indicator Minimum No 50% input for data quality Percentageindicator Preferred No 80% Input for data quality Percentage indicator

Ultrasonic Test Set-Up

Table 4 above identifies the nominal Ultrasonic parameters generallyused in this inspection. The Acquisition Operator may confirm theappropriate parameters have been loaded following the calibration run.

The temperature of the colt/plant during acquisition can change by anappreciable margin, with a typical range of 18 to 40 degrees and, insome instances, beyond this range. There is a corresponding change insound velocity within the couplant. Unlike other inspection methods,velocity change in the couplant can have a significant effect on theresults. Therefore the temperature at which the inspection is conductedshould generally be monitored. Acceptable temperature range forinspection in some embodiments is 15 to 45 C. If temperatures areencountered outside of this range, efforts to bring the temperaturewithin the specified range may be made,

A temperature sensor may be included in the hardware in someembodiments. FIGS. 79 and 80 show an example temperature sensor 7902connected to the manipulator 100 by means of a temperature sensorattachment clip 7904. The Neovision™ application may record thetemperature for each acquisition position.

Data Validation Indicator

Some embodiments may include a Data Validity Indicator software featureas an aid to the Acquisition Operator in evaluating the data set. TheIndicator displays the presence of signals in the interface gate andgate 1 for the OD and ID surfaces respectively.

Input parameters are:

-   -   Interface gate    -   Interface gate amplitude    -   Gate 1    -   Gate 1 amplitude    -   OD aperture size, minimum percentage, preferred percentage    -   ID aperture size, minimum percentage, preferred percentage

Input parameters may be adjusted in set-up mode.

Inspection Sequence Definition

A separate inspection sequence may be defined for the 2″ and 2.5″manipulators. The settings for the manipulators are given in Tables 5and 6 below.

TABLE 5 Inspection sequence parameters - 2.5″ manipulator ParameterValue Comments Start 0 Start position may differ from home position End245 Based on pipe diameter Scan Interval 0.5 Procedure requirement Speed10 Counts/mm 91.80 Calculated value Manipulator 47.45 Design valueradius Reduction ratio 3748.6 Calculated value Maximum current 500Related to maximum surge current - do not exceed Stall current 450Related to maximum continuous current Proportional Gain 2000 Determinedvia testing Integral Gain 80 Determined via testing Derivative Gain 1000Determined via testing

TABLE 6 Inspection sequence parameters - 2″ manipulator Parameter ValueComments Start 0 Start position may differ from home position End 195Based on pipe diameter Scan Interval 0.5 Procedure requirement Speed 10Counts/mm 65.64 Calculated value Manipulator 39.3 Design value radiusReduction ratio 2133.3 Calculated value Maximum current 500 Related tomaximum surge current - do not exceed Stall current 450 Related tomaximum continuous current Proportional Gain 2000 Determined via testingIntegral Gain 80 Determined via testing Derivative Gain 1000 Determinedvia testing

Scans and Inspection Zones

Due to offset between the feeder and the manipulator as well as surfacevariation of the feeder, the manipulator in some example embodiments canonly acquire UT signals in specific orientations. A compromise may beeffected wherein the manipulator is configured in a 0 degree mode toacquire intrados and extrados areas, a 6 degree forward configuration toacquire the left cheek and a 6 degree reverse to acquire the rightcheek. The UT signals may be degraded when the manipulator is scanningover areas other than the intended region.

The start location for the 0 degree scan is in some embodiments definedas the location where the transducer is directly over the right cheek.The manipulator is oriented with the cables facing toward the header(away from the Grayloc™) and the scan proceeds in the positive directionthrough the intrados of the bend to the left cheek, then to the extradosof the bend terminating on the right cheek.

The start location for the 6 degree forward scan is in some embodimentsdefined as the location where the transducer is slightly to the right ofthe fitting intrados. The orientation of the cables is the same as the 0degree configuration, facing toward the header, away from the Grayloc™.The scan proceeds from the intrados over the left cheek to just past theextrados centre line. The scan should generally acquire both intradosand extrados and not be less than 100 mm in length.

The start location for the 6 degree reverse scan is in some embodimentsdefined as the location where the transducer is slightly to the left ofthe fitting intrados. The orientation of the cables is opposite the 0degree configuration, facing away from the header, towards the Grayloc™.The scan proceeds from the intrados over the right cheek to just pastthe extrados centre line. The scan must acquire both intrados andextrados and not be less than 100 mm in length.

The Acquisition Operator may identify the scan type, start and length inthe Inspection Record.

In some embodiments having an adjustable reflector or mirror 154, thedual axis configuration may improve the capabilities of the system. Byarticulating the UT beam via a mirror 154, the tool may be capable ofgathering the required data in one scan for most configurations. In thecase of compound joints 2 scans may be required, one that tracks on thefirst fitting, and a second scan that track on the 2nd fitting.

Manipulator Set-up on Inspection Sample

The following steps may be carried out generally in all scan types:

-   -   (1) The platform operator may install the manipulator on the        feeder, ensuring that the manipulator halves meet and are        securely fastened. If direct access to the inspection area is        not possible the Platform Operator can install the manipulator        at an accessible area of the feeder and reposition the        manipulator over the area to be inspected. The drive enclosure        on the manipulator should generally be at the top of the feeder        for horizontal feeder runs, or with the gear train at the bottom        in case of vertical runs. The platform operator may also check        the seals on the manipulator to ensure the direction they are        flexed in is uniform.    -   (2) The platform operator may loop the cable over an adjacent        end fitting to relieve strain on the manipulator, ensuring that        there is sufficient transducer cable slack available to permit        rotation of the manipulator in the intended direction.    -   (3) The platform operator and the acquisition operator may        re-confirm the identity of the feeder to be inspected after the        manipulator has been installed on the feeder.    -   (4) Once manipulator placement is confirmed, the acquisition        operator may start the couplant pump to fill the manipulator.        The platform operator may check the manipulator seals for        excessive leakage. Adjustment of the seals may be required to        reduce leakage. The platform operator may be required to adjust        the flow rate to optimize couplant consumption via the valve on        the couplant flow solenoid assembly.    -   (5) Coincident with checking the seals, the Platform Operator        may also check and confirm that both halves of the manipulator        are securely latched. Excessive leakage and poor motion control        may be encountered if the halves are not fully engaged.

Inspection Process

A flowchart for the inspection process is given in FIG. 60. TheAcquisition Operator has the option of using different manipulatorscanning modes depending upon the circumstance encountered. Thefollowing steps may be carried out generally in all scan types:

-   -   (1) The Acquisition Operator may start the static scan once the        couplant pump has been started and couplant is confirmed to be        leaking from the manipulator. The B scan window should be        populated with linear B scan image, the A scan window should be        populated with an A scan from the B scan window. If the B scan        is present without the A scan, the Acquisition Operator may        re-establish the link of the A scan selection to the B scan        window. If neither window is populated, the Acquisition Operator        and Inspection Supervisor may investigate and remedy the cause        before proceeding. Once the A and B scans have been established        the Acquisition Operator should make a cursory assessment of the        scan quality to identify and correct any obvious deficiencies.    -   (2) The Acquisition Operator may jog the transducer to the        desired start location, if not using the Home position as the        start position. In the alternative the Platform Operator can        perform this task using the local control on the VIM. When the        transducer has been positioned at the scan start position the        Acquisition Operator may set Start position to zero. Again, the        Platform Operator may ensure sufficient cable slack and proper        cable feed for the intended direction of travel.    -   (3) If at any time the Platform Operator sees the possibility of        interference or restricted access for the inspection, the        Platform Operator may notify the Acquisition Operator. The        Acquisition Operator may choose to start the scan at a more        advantageous position if clearance is an issue.    -   (4) The Acquisition Operator may choose to perform an optional        test run. The purpose of the test run is to check the position        of weld, signal amplitude over weld cap, manipulator alignment,        presence of air bubbles or pockets, or other        conditions/artefacts. The suggested value for circumferential        resolution is 10 to 15 mm in some embodiments. Any adverse        conditions noted in the test run can be corrected before the        scan is conducted.    -   (5) The Acquisition Operator may initiate the scan from the        start position. The platform operator may monitor the progress        of the manipulator, paying close attention to proper cable feed.        The acquisition operator may monitor the main diagonal B scan        and corresponding A scans for data quality. In regions of        reduced clearance the transducer may contact adjacent        obstructions. It is usually preferable for the manipulator to        stall out rather than the transducer pushing the manipulator        aside. In the latter case, erroneous data may be recorded.    -   (6) The manipulator control may return the manipulator to the        start position upon completion of the scan provided a stall        condition has not been detected. If a stall condition has been        detected, the behaviour of the manipulator may be as per the        scan option chosen by the Acquisition Operator.    -   (7) The Acquisition Operator may review the completed file for        factors affecting scan acceptability. The Acquisition Operator        may shut off the couplant pump while reviewing the file. If the        scan is not acceptable the Acquisition and Platform Operators        may attempt to correct the deficiency and rescan the weld. There        are no specified limits to the number of attempts at acquiring        acceptable data. The Acquisition Operator may save data that        does not meet a set of quality criteria if a number of scan        attempts have been made. Notations describing the limitations        may be made in the inspection record.    -   (8) If the Acquisition Operator deems the file to be acceptable,        the Acquisition Operator may save the file and the Inspection        Record.    -   (9) The Acquisition Operator may drive the manipulator to home        position if it not there already.    -   (10) The Acquisition Operator may direct the Platform Operator        to either detach the manipulator or move it to the next location        if on the same feeder.    -   (11) The Acquisition and Platform Operators may perform the        calibration at specified intervals as noted above. Data obtained        subsequent to the most recent valid calibration scan may be        discarded and the affected welds may be re-inspected.

Extent of Inspection

The extent of the inspection may be as per scope defined above and inFIGS. 49-50. Note the inspection zone defined for these geometries isbased on a known position of the weld centreline. The manipulator may insome embodiments be built to track a cylindrical profile and may notnecessarily track the weld centreline on complex geometries. On thesegeometries, the weld centreline may follow a sinusoidal trajectory inthe scan data. Users may elect to reposition the manipulator for asecond pass if it is deemed necessary to obtain data from other areas ofthe feeder.

Recording Criteria

With respect to the NEOVISION™ application, all signals following theinterface signal may be recorded.

Evaluation and Acceptance Criteria

The Acquisition Operator may review the data file with respect to thefollowing criteria to determine scan acceptability. Given the number offactors contributing to signal quality, the Acquisition Operator maymonitor the data as it is acquired and spend time after acquisitionperforming any detailed review of selected areas.

The following criteria apply to the area of interest for the scan inquestion, i.e., extrados/intrados in the case of the 0 degreeconfiguration, left cheek for the 6 degree forward scan, right cheek forthe 6 degree reverse scan according to the example embodiments describedabove. Adverse conditions encountered in areas not relevant to the scanare generally not considered in data quality evaluation.

Signal Quality

(1) Skew & Offset: Skew conditions may occur when the transducer is notaligned with the inspection surface in the axial direction. Offset is asimilar condition, but may instead occur when the transducer, whileparallel to the pipe axis, is a lateral distance sideways from the pipeaxis. Both of these conditions may lead to a significant drop in ODsignal amplitude as well as splitting of the OD signal. Under theseconditions the ID signal may be completely lost. These conditions maybe, to a degree, inherent when inspecting tight elbows or joint to jointconfigurations, but should not generally exceed 40% of the totalcircumference. Repositioning the manipulator in the axial and/or radialposition with respect to the pipe surface may reduce the amount ofdistortion in the scan.

(2) Air bubbles: Air bubbles can occur in 3 places: on the inspectionsurface, on the transducer surface or oscillating in the water column.Air bubbles in general have a negative effect on data quality. Of thethree conditions, air bubbles on the pipe surface have the least effectdue to their localized nature and their tendency to be small in size.The larger of these bubbles may yield a response that causes acquisitionto occur prematurely. Surface air bubbles are excessive when they resultin an observable decrease in OD or ID signal amplitude. Air bubbles onthe pipe may be reduced by a combination proper cleaning of the pipesurface and use of a surfactant during the cleaning process.Alternately, moving the manipulator axially along the pipe such that theseals scrub the surface can dislodge the majority of the bubbles.

Air bubbles located on the transducer/mirror face are more significantinsofar as they constantly dampen the pulse from specific elements forthe entire duration of the inspection. This type of air bubble isdetected by an abnormal persistent drop in OD/ID signals particular to afew elements. This behaviour can be confirmed on the reference block.This type of air bubble may be unacceptable in some embodiments when itcauses a drop of 2 dB or more in the OD/ID signals for the length of thescan. Air bubbles on the transducer/mirror face may be mitigated bygently swabbing the surface of the transducer with an approved UT gelcouplant.

Air bubbles in the water column are evident as erratic triggering of thedata acquisition interface. The most common cause of this type of airbubble is air entrained in the couplant supply. Inappropriate triggeringof the data acquisition may lead to problems in the data analysisroutines and could, under some conditions, give rise to erroneousresults. Given the size of the water column, air bubbles in the watercolumn should not generally occupy more than a few individual scan linesof the entire data file in some embodiments. If air bubbles in the watercolumn are found in any appreciable quantity (more than 5% of scanlocations in some embodiments), the source of the air bubbles should beidentified and eliminated. Rescanning the affected weld may be conductedand noted in the inspection record.

(3) Air pockets: Air pockets may occur at the upper extremity of themanipulator and may be caused when the leak rate exceeds the couplantsupply rate. An air pocket is observed when there is a partial tocomplete loss of UT signal across the transducer on consecutive scanlines. If the loss of signal is isolated to few channels or is short induration (˜5 frames) the acquisition operator may accept the scan file.Otherwise it may be advisable to address the cause of the excessive leakrate and/or increase the flow to the manipulator and rescan the feederweld.

(4) Signal range limits: Factors such as position of the manipulator onthe feeder surface, weld cap height and feeder geometry may have asignificant influence on the range of water path distance in the scan.Excessive variation in the water path length may have an adverse effecton the scan quality. The most problematic situation may occur when thetransducer is too close to the inspection surface (less than 200 DP insome embodiments). In this case the interface signal may merge into therun down from the initial pulse of the transmitting element, potentiallyleading to a false trigger. Furthermore the interface signal at thispoint may be adjacent the near zone for the element, in which case thesignal amplitude is highly variable. Both of these conditions mayintroduce errors in the analysis process. The most significant effect,however, may be that the second water column interface will be capturedin the data. This feature may be erroneously identified as the backwallsince the amplitude may be greater than the true ID signals. In extremecases such as excessive weld bead height, there may be insufficientclearance between the transducers and the inspection surface resultingin damage to the transducer itself.

A potentially less problematic condition is where there is excessivedistance between the transducer and the inspection surface. This mayoccur on the opposite side of the feeder from the condition found in theabove paragraph. The effect on data analysis is more subtle as thefurther away the transducer is from the inspection surface, the morerestricted the viewing angle to any given feature.

One way to correct for range limit related problems is to reposition themanipulator with the intent of bring the manipulator close to the pipeon the long water column side while moving the manipulator away from thepipe on the short column side. The weld may be rescanned afterre-positioning the manipulator. Note that problems introduced byexcessive weld cap height might not be addressed by re-positioning themanipulator.

(5) Electrical Noise: Electrical noise in the scan may be identified asrandom (in few cases, repeating) signals in the one or more A scantraces. Under the assumption the noise is not correlated, i.e. does notform a repeating signal, the noise may be effectively averaged out inthe data analysis. Problems may occur if the amplitude of the noisebecomes high relative to the amplitude of the ID signals (within −6 dBof ID in some embodiments). The source of the electrical noise could befrom a number of areas related to transducer, cable or connector damageor in some instances may be related to the Micropulse™ unit itself.

If electrical noise is identified in scan data, one means to address theproblem is to replace the transducer or the Micropulse™ instrument andrescan the weld.

(6) Temperature Range: The temperature range for the data may be 15 to45 C in some embodiments. Temperature outside this range may causeinaccurate results. Acquisition Operators may rescan such that thetemperatures are within these bounds.

(7) Missing/dead elements: Missing or dead elements can arise from thenatural aging of the transducer, damage to the cable or connector, or afault in the Micropulse™ instrument. The scan is generally acceptable ifno greater than 13 dead/missing elements are identified, provided thatof the 13 elements, no more than 3 are immediately adjacent each other.If the fault can be traced to the Micropulse™ it is generallyrecommended that the Micropulse™ be removed for repair and the weldrescanned.

(8) Interface amplitude: The gain may be set to a level that undernominal conditions interface amplitude is saturated by 6 dB. The purposeof this setting is to ensure the weld cap interface signal remains at asufficiently high level to trigger data acquisition. Excessive gainsettings can be problematic for subsequent data analysis. If the weldcap signals are insufficient to trigger data acquisition, the gain maybe raised, preferably not more than 6 dB in some embodiments, along withincreasing the interface gate amplitude. Caution is advised whenlowering gate amplitude since unwanted signals may now cause falseacquisition.

(9) ID signal amplitude: Under nominal conditions, transducer normal tosurface, stable water column, inspection surface at intermediate range,the pipe ID signals should attain an amplitude of approximately 10% FSHin some embodiments. If the pipe ID signals deviate by more than +/−6 dBfrom this value the cause may be investigated and corrected if possible(air bubbles, loose surface dirt).

(10) Inspection Coverage: Inadequate inspection area cover with respectto the desired coverage may require repositioning of the manipulator andrescanning. In some instances the inspection geometry may limit one-passcoverage necessitating two passes to achieve desired coverage. If thisis the case two files may be used for the inspection with appropriatenotations made in the inspection record. If obstructions limit theability to position the manipulator to attain the required coverage,appropriate notations may be made in the inspection record.

(11) Start/End Correspondence: The beginning and final frame of a fullcircumference scan may display the same UT feature at the same locationto within +/−2.0 mm in the axial direction and within +/−50 DP in theradial direction in some embodiments. If the start and end frames do notcorrespond to within these limits, the manipulator may have beendisplaced during scanning. If so, the scan may be repeated.

Data Files

The Acquisition Operator may export the data file to the analysis site.The analysis site may have multiple redundant storage devices to retainthe data files. Analysis of the data may occur upon completion of thedata acquisition phase.

Inspection Record Contents

The Inspection Record in some embodiments is a document that logs theparameters for the inspection conducted. There may be a separateinspection record for each feeder inspected. Note there may be multiplewelds associated with any given feeder.

The following information may be contained in the inspection record:Station, Unit, Date, Time, Face, Feeder, Weld, File Name, Transducer,Manipulator, Micropulse Serial, Vault Interface, ID, Serial Number,Number, Module SN, Acquisition, Platform, Reference Tube ID, CalibrationFile, Operator, Operator, Resolution, Data Quality, Scan start, Scanlength, Scan Type, Restrictions, a sketch of the actual feederidentifying the location and nature of any scanning restrictions, andany other relevant information about the inspection.

Data Analysis

The data acquisition method for inspection of feeder weld profiles inthe present described examples is the application of the Full MatrixCapture (FMC) inspection technique. This method uses multipleindependent elements to transmit and receive sound, and thus acquires avery large data array. The array is so large as to effectively precludemanual analysis of the data.

The basis for this analysis method is sequential back propagation oftime indices in the source A scans. This technique is sometimesgenerically referred to as the Total Focus Method (TFM). The result ofthe analysis method is Computed Ultrasonic Tomography (CUT).

The following section defines the procedure by which FMC data may beanalyzed using the NEOVISION™ software application in an exampleembodiment. It provides the procedures for using the NEOVISION™ softwareapplication as an analysis tool in the inspection of a feeder weld area.The data to be analysed is obtained from the inspection of feeder weldjoints. This procedure is applicable to FMC data sets obtained from:pipe to pipe, pipe to fitting, fitting to fitting, and fitting toGrayloc™ welds. See FIG. 49 as an example of a section of a fitting tofitting weld. The intended coverage is 20 mm on either side of the weldcentreline. However geometric limitations may constrain the region ofaccess.

This example embodiment accommodates the analysis of data files obtainedusing both 0 and 6 degree offsets as described in the exampleacquisition procedure above.

FMC UT data from the inspection campaign is provided prior to analysis.A multi processor blade server system may be used to support theNeovision™ analysis software application. This system in someembodiments is linked by an appropriate network to both the acquisitionand analysis sites. The network connections should be able to support adata transfer rate 1 GBit/s.

Equipment and Tools

Table 7 below lists example equipment used under this procedure in anexample embodiment.

TABLE 7 Hardware and software required for data analysis Quantity ItemDescription 1 per user Desktop PC (remote) 4 GByte memory, Core2 Duo 2.3GHz or above, 32 bit Windows XP ™, 500 GByte HDD, 1 GBit Ethernetinterface, Dual 19″ monitors 1 Client PC Configured as required tosupport the Desktop PC 1 Storage Device Network based local device forstorage of inspection data and processed results. Suggested size 5 TBminimum 1 Analysis Blade Server Analysis system consisting of 2 serversand 28 dual processor quad core blades 1 Linux ™ OS SUSE 1 Matlab ™Version 2008b 1 Windows ™ 64 bit XP SP3 Server 1 NEOVISION ™ Version 1.0or most recent qualified release

Verification Data Analysis

Verification of the analysis results is achieved by having a secondqualified analyst independently perform analysis on the same data set.In this strategy the initial work is called the primary analysis whilethe following work is termed the secondary analysis.

Any qualified analyst may perform either primary or secondary analysison any data set. Generally, the same analyst does not perform bothprimary and secondary analysis on the same data set.

The value and location of the minimum thickness for both primary andsecondary analysis are compared. If the results are within 0.06 mm forthe same location in some embodiments the results of the lesser of thetwo are reported. If the results do not agree within these bounds athird analyst, Lead Analyst, may perform independent evaluation of thedata.

Procedure

A Results Coordinator may be responsible to ensure the analysis isconducted per this procedure.

Analysis of FMC Data Sets—Procedure

Analysis of FMC data sets is a serial chain of processing steps, theresults of any given step are built upon the results of the precedingsteps. Thus any inaccuracy or loss of data may significantly impair allsubsequent steps. The reliability of the results is a function of theinput data quality.

The following two sections discuss the Analysis workflow and Analysisdata flow. The workflow describes the steps taken by the analyst inorder to generate the final results. See the flow chart presented inFIG. 61. The data flow describes the processing performed by thesoftware. The data flow is summarized here to provide backgroundinformation when determining the processing parameters. The dataanalysis algorithms are discussed in the previous sections above.

Analysis Workflow Overview

The first step in the workflow is initiated in the Data Acquisitionphase. The Acquisition Operator uploads a completed scan file that meetsthe acceptability criteria to the Gateway server. A completed InspectionRecord generally also accompanies the data file.

The Analyst may then retrieve the UT data file and the correspondingInspection Record form and initiate an Analysis Record form for the job.The Analyst may then load the UT data file and review the data qualityas per acceptance criteria. Any deviations or exceptions from theacceptance criteria should generally already be identified in theInspection Record for the file. If so, the exceptions may be noted inthe Analysis Record. If exceptions to data quality are identified in theUT data that are not reflected in the Inspection Record, the Analyst mayinitiate a request for rescan of the weld.

The Analyst may then review the file recording the UT parameters thatinfluence the analysis settings. The processing parameters may beadjusted as per the guidelines described above. Processing parametersmay be recorded in the Analysis record. The job is then submitted forprocessing.

When the processing has been completed the Analyst may retrieve theResults file. The Analyst may initiate a Results Record form. TheAnalyst may evaluate the results against a number of criteria foroverall validity. The data may be re-processed with different series ofparameters to address areas that do not meet the validity criteria.

When the Analyst has completed evaluation and no further gains inresults are obtained, the Analyst may record the minimum thicknessinformation in the Results Record. The Analyst may also produce trendfile(s) as specified below.

Analysis Data Flow Overview

The analysis data flow is depicted in FIG. 62. In the example embodimentdescribed currently, the process is initiated with the transfer of datafrom the acquisition site to the Gateway server. The job list willupdate as the Gateway receives the data, preprocessing of the databegins immediately. Pre-processing includes such steps as data fileconversion and digital filtering.

The files for the individual scan frames are downloaded to availableprocessors located in the Blade servers. The following steps areperformed in sequential order.

-   -   Up-sampling the data set to equivalent of 100 MHz sampling rate.    -   Compensation for DC offset and digital filtering of UT data    -   OD Intensity Map formation. The OD Intensity map is formed by        evaluating the time indices to each defined point in the        inspection volume for each element as per the parameters set.        The time indices are summed for each transmitter-receiver pair        and the amplitude of the A scan at that time index is summed        into the intensity map. The summation process is repeated for        all transmitter-receiver combinations that are valid for the        specified point in the inspection volume.    -   OD Surface Recognition. The OD surface is defined using the        parameters provided. The X,Z coordinate pairs are generated by        the algorithm. Interpolation may be conducted on low level or        missing portions of the Intensity Map. The OD Surface is        smoothed as an input to the next processing step. A separate        smoothing value is applied for output purposes. Note the OD        Surface X,Z pairs form part of the Results output.    -   ID Intensity Map formation. The ID Intensity Map is calculated        applying the parameters modified. The ID Intensity Map is        calculated in a similar manner as the OD Intensity Map however        the X,Z coordinates of the OD surface are used to determine the        appropriate time indices for the various transmitter-receiver        pairs. Options such as OD signal suppression and signal        normalization are implemented at this stage prior to forming the        ID Intensity Map.    -   ID Surface Recognition. The ID surface is defined using the        parameters provided. The X,Z coordinate pairs are generated by        the algorithm. Interpolation may be conducted on low level or        missing portions of the Intensity Map. The X,Z coordinate pairs        are smoothed for output purposes. Note the ID Surface X,Z        coordinate pairs form part of the Results output.

The processed OD and ID coordinates are transferred back to the Gatewayserver as part of the Results file. This process is repeated for allindividual frame files in the Gateway queue. When all available FMCframe files have been processed the job status on the Gateway server isupdated to “post-processing”. A short interval after the job status isupdated to “complete”.

Results Review—Screen Layout

Main Results window

The Main results window is illustrated in FIG. 63. The Analyst can usethis window to step through the results for each one of the frames. Thecontents of the Main results window are listed below:

(1) OD Intensity map is plotted in the upper left hand of the Resultswindow(2) ID Intensity map is plotted in the middle of the left hand of theResults window(3) The combined OD/ID profiles (X,Z coordinates) are plotted in thelower left hand of the Results window.(4) Acquisition, Analysis and File information are listed in the lowerright hand of the Results window.(5) A listing of all TMIN values and locations for each frame, locatedin the mid right hand side of the Results window.(6) The global TMIN is found in the upper right side of the Resultsview.(7) Controls for scrolling through and sorting the Results data arefound in the upper right hand.(8) Options for generating various 3D views are located in the upperright hand of the Results view.

3D Generation Windows

The 3D Generation windows are a group that provide a 3D reconstructionof the inspected volume. The reconstruction is achieved by plotting theOD and ID profiles either in a linear space or in a radial format. Theseviews are exceptionally useful when assessing OD surface or ID surfacerelated features or evaluating thinning trends. The usefulness becomescompromised however when an appreciable number of the scan profiles havesignificant interpolation or incorrectly identify Intensity mapartefacts.

3D Window

The 3D window provides the base 3D development and 3D radial views. Anexample of this window is given in FIG. 64. Display options located onthe right side of the window permit the Analyst to vary the degree ofdetail in the view, the content of the view, surface filtering, pan,rotation, zoom and the colour scale applied.

Overview Window

The Overview window provides the Analyst with the 3D radialreconstruction of the inspection volume. In addition, this view alsoprovides the Analyst with axial and radial profiles as plotted withrespect to the inspection volume. This is useful when evaluatingthinning trends on either surface. Again the plots may be panned,rotated, zoomed with different surfaces and detail levels presented. SeeFIG. 65 as an example of the Overview window.

Pop Up Window

The Pop-up window provides the Analyst with a dedicated window for anyof the selected view types. The purpose of this window is to optimizethe view for evaluation or reporting purposes. See FIG. 66 for anexample of the Pop-up window.

Further Details of Data Analysis Using NEOVISION™ Analysis Method

The following is a brief synopsis of the Analysis method as per FIG. 62.Further detail for each step is provided in the paragraph identified forthat step.

Data analysis can begin with notification that a file has status haschanged from “Job Pre-processing” to “Job Ready for Processing”. TheAnalyst may be assigned the role of primary or secondary analysis on agiven file provided they do not perform the alternate role on the samefile. The Analyst may initiate an Analysis Record form for the file tobe analyzed.

Step 1: Calibration Verification

If the Analyst has not yet verified the calibration scan and record forthe current session, the Analyst may retrieve both the calibration datafile and record. Both of these records may be checked for metrics asoutlined in Table 8 below. If the calibration is not valid, the Analystmay notify the Lead Analyst to resolve the issue with the Work PackageOwner and Acquisition FLM.

TABLE 8 List of Calibration variables and criteria Factor Value RangeComments Relationship to Not Not applicable Must be same transducer,acquisition set-up applicable equipment and personnel as used for dataacquisition Validity Interval Data acquired within 4 hours ofCalibration activity Element Functionality Not No more than 13 missing,no Reject transducer if outside applicable more than 3 adjacent missingspec Probe delay 32 DP +/−2 DP Note - when at 100 MHz sampling rateWater column DAC 80% FSH 50% to 99% FSH Water column As per +/−0.01 mmNote - when at 100 MHz measurement block, see sampling rate Appendix BMetal path measurement As per +/−0.03 mm Note - when at 100 MHz block,see sampling rate Appendix B Transducer—manipulator Not +/−1 degree Peakto occur on the zero alignment applicable degree flat Temperature sensorNot 15-45° C. Observed to be functioning function applicable andrational Encoder check 0, 195/245 +/−0.3 mm Value dependent uponmanipulator in use

Step 2: Inspection Data Quality and Record Verification

The Analyst may review the UT data file and corresponding InspectionRecord as per the criteria provided. Any exceptions to data quality maybe noted in the Inspection Record. If exceptions are not identified inthe Inspection Record, the Analyst may request a rescan.

TABLE 9 Summary of scan data quality factors Number Issue ConditionCriteria 1 Transducer - Skew No more than a cumulative total of 40% ofFeeder Misalignment the scan missing 1 Transducer - Offset No more thana cumulative total of 40% of Feeder Misalignment the scan missing 2 AirBubbles Pipe surface: No appreciable reduction in interface or ID signalamplitude 2 Air Bubbles Transducer/ Not acceptable Mirror surface 2 AirBubbles Water Column Not greater than 5 scan lines mistriggered 3 AirPockets Horizontal Air pocket less than 5 consecutive scan lines 3 AirPockets Vertical Less than 10 elements and no greater than 40 scan lines4 Signal Range Limits Near Interface signal not closer than 200 DP 4Signal Range Limits Far Sufficiently close to trigger Interface Gate,should not exceed 900 DP 5 Electrical Noise Not to exceed 5% FSH ifuncorrelated, to 2% FSH if correlated 6 Temperature Reading Within rangeof 15 to 45 C and rational 7 Missing - Dead No more than 13 total, andnot greater than Elements 3 adjacent 8 Interface Amplitude Approximately6 dB beyond 100% FSH on straight pipe sections or sufficient to reliablytrigger interface gate on weld cap 9 ID Signal Amplitude Under pipeObservable in Linear Electronic B scan material 10 Inspection CoverageAxial Transducer centred over weld - subject to fitting geometry andobstructions 10 Inspection Coverage Circumferential Full circumferencescanned - subject to obstructions 11 Start/End Axial Start & End UTfeatures in same axial Correspondence position +/−1.0 mm 11 Start/EndRadial Start & End UT features in same radial Correspondence position+/−50 DP

Step 3: Inspection Data Characteristics Measurement

The Analyst may repeat the review of the UT data measuring specificcharacteristics of the feeder. These characteristics are then used as abasis for modifying the analysis parameters as required. Some specificcharacteristics that can be measured are set out in Table 10 below.

TABLE 10 UT Data characteristics related to processing parameters.Processing Condition Parameter(s) Comment Water Maximum X Min, X MaxIncreasing water column path to Aperture reduces the effective apertureof interface Height the transducer. Excessive Height can introduce IDartefacts if the 2^(nd) interface is recorded in the UT data.Insufficient Height and X Max can clip ID Intensity map images in areasof thick cross section Water Minimum X Min, X Max While reducing thewater path to Aperture column height allows signals interfaceDirectivity to be observed across a wider Cutoff aperture, the 2^(nd)water column Height interface will limit the maximum thickness that canbe imaged. The wider aperture is only effective up to the point whereDirectivity Cutoff limits the number of channels used. InterfaceNormalization Low interface signals amplitude signal OD suppression maybe compensated for by amplitude using Normalization - with the potentialof introducing artefacts OD suppression will improve S/N ratio on IDonly if minimal/correct interpolation of OD is achieved ID signal UnderNormalization Reducing the threshold levels used amplitude in weld fornormalization may enhance Linear cap the ID Intensity map Electronic Bscan ID signal Under Directivity Changing both the Directivity amplitudein parent Cutoff Cutoff and Max Tx-Rx Linear material Maximum Differencemay aid in the Electronic B Transmitter- formation of ID Intensity map.scan Receiver Difference Weld cap width Maximum The aspect ratio (widthto height) Transmitter- is an indication of what may be Receiverobserved under the weld cap, Difference the higher the ratio, the betterNormalization the reconstruction of the ID Weld cap height NA Onlyrelevant with respect to weld cap width, see above Weld capNormalization Indication of ripple, high interface modulation may defeatID reconstruction in one or

Step 4: Parameter Variation and Processing

The default parameters can be applied for the first pass in the Analysisprocess. Specific Inspection data characteristics may warrant changingsome processing parameters. The parameters used for each pass may besaved to a unique file associated with the Results file.

Step 5: Results Review and Verification

The Analyst may retrieve and review the results. Depending upon thefindings of the Results review, the Analyst may choose to re-process thedata one or more times for the entire file or subsets of the file. TheAnalyst may save each set of processing parameters under a separate filename for each round of processing.

Step 6: Reporting Results and Documentation

The Analyst may record specific results information in the Resultsrecord. The Analyst may also create a Trend file. Supplementary outputsmay be included as well.

Indices Used for Determining Acquired UT Data Quality

In some embodiments, the software will provide the user with additionalinformation to aid the analyst in determining the quality of acquired UTdata. In some embodiments, this analysis may take place during Step 5:Results review and verification.

In an example embodiment, two indices are provided to the analyst to aidin the analysis. The first index is referred to as the “TheoreticalQuality Index”. This is based on the geometry of the detected ODsurface, and indicates the theoretical best possible quality levelachievable for ID UT. The second index is referred to as the “ActualQuality Index”. This is based on the ID intensity map from the collectedUT data.

The Actual Quality Index is generated using the collected UT data.First, the software takes the average of the whole intensity map,ignoring some small areas based on the detected surface. Specifically,the ignored areas are those that extend downwards in the Y axis from thedetected surface on the intensity map. By ignoring these areas whencalculating the average, the calculation removes all parts of theintensity map that contribute to the detected surfaces. The lower theaverage intensity of the rest of the intensity map is, the better the“Actual Quality Index” value will be. To arrive at the Actual QualityIndex value, this calculated average is compared to one or both of twonumbers:

-   1) the maximum intensity found anywhere in the intensity map (which    will likely be found in one of the areas ignored by the averaging),    and/or-   2) the maximum possible intensity, based on theoretical UT values    that could be achieved based on OD geometry.

FIG. 78 shows an example implementation of generating the Actual QualityIndex from an intensity map. The detected boundary 7802 is used toidentify those areas 7804 extending downwardly therefrom on the Y axis.These areas 7804 are ignored, and only the remaining areas 7806 are usedto generate an average intensity value. The lower this average intensityis, the better the Actual Quality Index.

Processing Software for Pipe Weld Inspection

Software used to perform the various procedures is described in detailbelow as part of an example embodiment. The tables A1 to A10 at the endof the Description provide example functions and parameters to be usedwithin the context of this example embodiment. The sections belowdescribing the software used in this example embodiment are intended tobe illustrative only, as a possible implementation of the systemdescribed in more general terms above.

Software Architecture

The Feeder Weld Area Thickness Measurement Tool (WPIT) contains asoftware suite to acquire, analyze and display feeder weld area profilesto determine minimum feeder thicknesses caused by Flow AssistedCorrosion.

Neovision™ is the primary user interface software, responsible foracquisition of the ultrasonic data, control of the probe manipulator,setup of the analysis parameters, communication between itself and theGrid Middleware program in Gateway mode to submit analysis job andretrieve combined result, and initiation of the Results Display programto display the analyzed result. This software can only be executed underthe Windows™ operating system.

The Grid Middleware program is a java program and it has 2 modes:Gateway mode and Agent mode.

In Gateway mode, it communicates with Neovision™ and receives ananalysis job request from it. It breaks down the Neovision™ ultrasonicdata file into individual FMC data files by invoking the Exporterprogram. It controls the flow of data to the Grid Middleware Agent, bysending FMC data out and receiving FMC result files back. It then mergesall the FMC result files into one combined result file by invoking theMerger program. The combined result file is then sent back to Neovision™based on user request.

In Agent mode, it receives FMC data files from the Gateway and starts upa set of Analysis programs so that the data can be analyzed. It reportsback to the Gateway on its status and the statuses of the Analysisprograms. When the FMC result files are created by the Analysisprograms, it sends the result files back to the Gateway. It is designedso that each server needs one instance of the Agent and the Agentcontrols and invokes a set of Analysis programs as defined and limitedby configuration settings.

The Grid Middleware program is written in Java and it can be run onWindows™ operating system or Linux™ operating system. For Gateway mode,it must be executed on Windows operating system since it needs to invokethe Exporter program, which only works on Windows™ OS. A typicaldeployment includes a 64-bits Windows™ server which runs the GridMiddleware in Gateway mode, and a farm of 64-bits Linux™ Servers whichrun the Grid Middleware in Agent mode. For a small-scale deployment, theGrid middleware will be executed on the same machine as the Neovision™program and it will contain one instance of the Grid Middleware runningin Gateway mode and one instance of the Grid Middleware running in Agentmode.

The Exporter program exports the Neovision™ data file into a set of FMCdata files for the Analysis program. The FMC data file contains only oneset of FMC signals and it is saved as MATLAB™ data file. These FMC datafiles will be used as input file for the Analysis program. This programcan only be executed under Windows™ OS. This program is invoked by theGrid Middleware program in Gateway mode.

The Analysis program is a MATLAB™ stand-alone executable program whichcan be run on Windows™ operating system or Linux™ operating system. Witha single set of FMC data, it calculates 2D intensity map for the OD anddetermines the OD boundary definition. When the OD boundary is defined,it then calculates the 2D intensity map for the ID and determines the IDboundary definition. Using the OD and ID boundary definitions, itcalculates the minimum thickness (TMin) for this pair of OD and IDboundaries. It then converts the reportable results into a standardizedformat and saves them. The Analysis program outputs a single result filewhich contains all the reportable results. It is a highly memoryintensive and CPU intensive program. This program is run by the GridMiddleware program in Agent mode.

The Merger program takes output analysis result files from the Analysisprogram and combines all the reportable results to create a singlecombined result output file. By combining the OD and ID boundaries fromeach profile, the Merger program can reconstruct a 3D representation ofthe scan and recreate the OD and ID surfaces. The Merger program iswritten in MATLAB™ and it is a MATLAB™ stand-alone executable program.It is run by the Middleware in Gateway mode.

The Result Display program displays the combined result output file sothat the user can inspect and interrogate the result. It displays the 2DIntensity Maps, the OD and ID boundaries, the TMin for each profile andthe absolute TMin for the whole data file. It also displays the 3Drepresentation of the scan to give user a better understanding of thedata. The Result Display program provides a simple user interface sothat user can filter out unreasonable TMin values and profiles. Userscan create the thickness trend data file and export the filtered data toother CAD (Computer-Aided Design) programs for further investigationsuch as stress analysis. It runs on the same machine that runsNeovision™. It is a MATLAB stand-alone executable program and it isinvoked from Neovision™.

Data Pathway

Here are the data flow steps when analyzing the Neovision data file:

(1) After the Neovision™ data file is acquired, either the acquisitionoperator or the analyst can submit the valid data file into Neovision™and Neovision™ will send the data file to Grid Middleware Gateway modeand create a job for this data file.(2) The analyst selects the job and inspects the data file. The analystenters the analysis parameters after reviewing the data file and startsthe job. Neovision™ sends a request to the Grid Middleware and the GridMiddleware Gateway agent will start the analysis by calling the Exporterprogram(3) After the Exporter program converts the Neovision™ data file intoindividual FMC data files in MATLAB™ format, it sends them back toGateway.(4) When the Gateway finds an available Agent machine, it will send FMC.MAT files to the Grid Middleware Agent.(5) The Agent creates a node and invokes the Analysis program with theFMC .MAT file as one of the input parameters.(6) After the Analysis program completes its calculation, it sends theResult FMC .MAT file back to the Agent.(7) The Agent sends the Result FMC .MAT file to the Gateway and informsthe Gateway that it has an available node for more analysis computation.(8) When Gateway receives all Result FMC .MAT files for the Neovision™data file, it will invoke the Merger program and send it all the ResultFMC .MAT files.(9) The Merger program will extract reportable data and merge individualresults into a meaningful order and output a Combined Result file backto the Gateway.(10) The Gateway informs Neovision™ that the result is ready. The userthen retrieves the Combined Result file from the Gateway via Neovision™and saves a local copy of the Combined Result file.(11) After Neovision™ receives the Combined Result file, it invokes theResults Display program to display the data. The user can review,inspect, modify and export the data in the Result Display program.

Grid Middleware Component

The grid middleware component can be run in two modes which behave verydifferently. One is the gateway and the other is the agent. Afunctioning analysis system will have one instance of Neovision™, onegateway, and one or more agents. The gateway receives messages from bothNeovision™ and the agent(s) during analysis. Generally, the gateway doesnot initiate communications, though there are exceptions to this.

FMC Data Set Analysis

Analysis of the FMC data is implemented as described above.

Where the acquisition software delivers multiple FMC data-sets to theanalysis software, the analysis software analyzes each FMC data-setreceived independently and prescriptively. Thus, a description of theanalysis of a single FMC data-set is sufficient to describe the analysisof all FMC data-sets, in that the analysis process for all datasets isidentical. Upon completion of the analysis of the FMC data-sets, theoutput (OD and ID boundary coordinates) are input into the GraphicalOutput Process.

Graphical Output Process Create Profile-Based Result

The Profile-Based Result is based on the analysis result. Data isremapped and axes values are changed as needed. Thickness values foreach profile are created. The scan position and other profile basedinformation are created in this section.

It takes a small amount of time (about 3 seconds at maximum resolution)for each profile to create the Profile-Based Result. However, to create460 profiles together, even using MATLAB™ Vectorization technique, itwill still take a significant amount of time in the whole process. Inorder to shorten the total execution time, the Profile-Based Result iscreated at the end of the analysis program. So the Profile-Based Resultis created in parallel without any significant delay on the executiontime.

Merge Result

Merge Result is a stand-alone MATLAB™ executable. It merges individualProfile-Based Results in a scan and creates a result file. If the resultfile already exists, it will merge the specified profiles ofProfile-Based Results into the result file.

One of the main tasks of Merge Result is to reconstruct the 3Drepresentation of the OD and ID. Since MATLAB™ uses a 2D matrix torepresent any surface and uses the matrix row and column indices tomaintain the logical relative positions between data points, MergeResult will merge the OD and ID based on their relative position byfirst determining the global spacing resolution and the limits. Theresult from each profile is then remapped, and valid set of data pointsis inserted into the final OD and ID matrices.

The final profiles for the OD and ID are saved into matrices based ontheir profile ordering. A sorting vector is calculated based onprofile's position and their relative positions so that it can be usedwhen displaying the OD and ID in 3D views.

All Profile-Based Results are saved into the result file as differentstructures. All the useful information is stored in the merged resultfile, so there is no need to keep the output file from the analysiscommand line program. Invalid, partial (OD only), and empty profiles aremerged into the result as null value when the required data does notexist.

Merge Result gathers each profile's minimum thickness and saves theminto a matrix so that it is easier for the Display Program to access theinformation.

One of the limitations of the Merge Result is that the Profile-BasedResult from different profiles must have the same spacing resolution.Merge Result will skip the profile if the spacing settings are not thesame.

Also, the global spacing resolution and the limits are determined whenthe result file is being created by iterating the profiles until thefirst valid profile that has the information is found. Even if newerProfile-Based Results are merged in which have larger ranges, the MergeResult will only merge based on the limits defined previously. If theuser wants to re-create a different spacing resolution and differentrange limit of data, the result file must be removed first so that theglobal spacing resolutions and limits can be re-determined. Within theFP6 analysis steps, this requires the user to create a new job so thatthere will not have any existing result file and resubmit the scan foranalysis again.

Display Result Main Window

This is the main GUI for the Display Result program. All other displaywindows are created based on this window. The user can invoke the restof the display windows from this window and the user can also modifyTMin and export data from this window.

It has an initialize function (Initialize) to setup data by reading theresult file and cache the data into memory. It loads global setting froma settings file, which defines most of the display properties and thetrend output properties. It loads an analysis parameter mapping file sothat when the program displays the internal analysis parameters, theparameter names can be the same or similar to the one used in theanalysis parameter window in NeoVision™.

This window displays Profile-Based Results by accessing the individualstructure for each profile saved in the result file. They are the ODintensity map, ID intensity map, and the OD & ID profiles. It alsodisplays the TMin information and the parameter values for the scansettings, the analysis settings and the profile information.

When the user launches the sub-display windows such as popup, overviewor 3D, the program stores the handles from those figures (handlePopup,handleOverview, and handle3D) so that it could close all sub-windowswhen it is terminated. It also uses this list of handles to transmit theModified TMin information to the sub-windows.

A custom data cursor is added into the program. All windows use the samecustom function, CursorUpdateText, to display the data cursor.

There is no sortable ‘uitable’ control in MATLAB™ so the software has tore-create the feature manually for the TMin lists. Based on the buttonthe user clicks, different sorting parameter values are set and thesortUpdateListGUI amd Display™ in are called to perform the sorting anddisplay.

Display Popup Window

The Popup window is based on the main window and this is the only windowthat can have more than 1 instance within the Analysis Display program.

The main usage of this window is to allow the maximum screen resolutionby displaying a single figure only. The user can view a larger versionof any of the figures shown in other display windows.

Similar to other sub-windows, the Popup window can be invoked directlyand a stand-alone executable program can be created.

Display Overview Window

The Overview window is based on the main window and it is a singletonwindow.

The main usage of this window is to provide a high level overview of thescan. Along with the 3D view of the scan, it provides the cross sectionview and the axial view of the scan so that user can inspect the result.Refer to the 3D window for more details and description of the 3D View.

Similar to the popup window and the 3D window, a median filter can beapplied to the display data in the circumferential direction. Thisshould reduce noise and data error so that user can review the dataeffectively.

Similar to the popup window and the 3D window, four levels of datasampling are defined. The default level is medium and it should have thebest detail and performance combination.

The viewing plane of the cross section view and the axial view are alsodrawn on the 3D view to enhance the visual representation of the data.

Similar to other sub-windows, the Overview window can be invokeddirectly and a stand-alone executable program can be created.

Display 3D Window

The 3D window is based on the main window and it is a singleton window.The main usage of this window is to provide the 3D representation of thescan. It contains the developmental view and 3D view. The developmentalview displays all the profiles of data based on their profile orderingand displays them side by side. The 3D view is the 3D reconstruction ofthe scan.

Both views can be displayed as ‘surf’ or ‘mesh’ objects in MATLAB™drawing. For ‘surf’, color can be used to represent the thickness of thefeeder, or the surface can be drawn monotonically to review the detailof the surface.

Similar to the popup window and the Overview window, a median filter canbe applied to the display data in the circumferential direction. Thisshould reduce noise and data error so that the user can review the dataeffectively.

Similar to the popup window and the Overview window, four levels of datasampling are defined. The default level is medium and it should have thebest detail and performance combination.

Similar to other sub-windows, the Overview window can be invokeddirectly and a stand-alone executable program can be created.

Modify TMin

The functionality of Modify TMin is handled by the LoadModifiedTMin,SaveModifiedTMin, DisplayTMin, TMinUitableCellEditCallback andmenuui_ExportToFigures_Callback functions.

LoadModifiedTMin reads the MATLAB™ data file and loads modified TMininformation into the program.

SaveModifiedTMin writes modified TMin information into data file.

DisplayTMin will recreate the TMin lists based on sorting mode andignore list, and display the TMin lists on the main window.

TMinUitableCellEditCallback is invoked when the user modifies the localTMin list by selecting the ignore checkbox to remove or re-add TMinsfrom the list. This function will update the internal definition of theTMin list. Note that when the user removes a TMin from the list, itremoves that profile of the scan from the display, not just the TMindefinition. The profile removal will affect the display and the exportresult as those profiles will be omitted.

menuui_ExportToFigures_Callback is invoked when the user selects the‘Export Modified TMins To Other Window(s)’ option from the menu. Themodified TMin list will be sent to all sub-windows and the data on thosewindows will be updated. It is a two part process where the user willneed to refresh individual sub-windows to display the updated TMininformation.

Export 3D Point Cloud

The Export 3D Point Cloud exports the OD and ID surface as point cloudvalues.

The output filenames will be selected by the user and the OD and IDsurfaces will be saved into separate output file with file extension.xyz, which is a defined point cloud file extension for SolidWorks™.

The output file format contain a simple point definition per line andthe point is defined as ‘x y z’, where x, y, z are the coordination ofthe point.

Export Trend Result

Export Trend Result exports the trend information of the scan to anExcel™ file.

Any existing export file will be deleted.

The trend information contains the channel name, the global TMin list,and the TMin Trend.

The global TMin list contains the following fields:

1. Profile Number

2. TMin

3. Cric Position (mm)

4. OD Axial Position (X in mm)

5. OD Depth (Z in mm)

6. IF Axial Position (X in mm)

7. ID Depth (z in mm)

The TMin Trend contains equally spaced thickness values from the scan.Each line represents a scan profile with the following fields:

1. Profile Number

2. Cric Position (mm)

3. Axial Positions (mm)

Axial Positions are equally spaced throughout the profile. When thethickness is not available for the particular sampling point, it will bemarked as ‘NaN’. When the thickness for a particular sampling point isonly available using interpolated values, it will be marked as ‘IV’. Ifa particular sampling point is ignored based on the modified TMinselection, it will be marked as ‘Ign’. Currently the ignored selectiononly applies to the profile so the whole row will be marked as ‘Ign’.All trend information will be saved into a new tab in the Excel™ fileand the tab name is the channel name.

TABLE A1 Preprocessing Functions Input Output Function Data Input DataData Output Name Input Data Class Description Data Class Descriptionfiltfilthd 1. Three 1. Single 1. Full matrix Three Single Filtered fulldimensional 2. Filter RF data-set. dimensional matrix Matrix object 2.Filter matrix. RF data-set. (real). object which 2. Digital definessoftware frequency filter. attenuating properties of the digital filter.ndimhilbert Three Single Full matrix Three Single Full matrixdimensional RF data-set. dimensional (complex) analytic Matrix. matrixtime-domain data-set.

TABLE A2 OD Imaging Parameters Variable Class Description blockSizeSingle A Scalar defining the size of the physical aperture used in SFMimaging. This scalar can range from 1 to the size of the array.directivityCutoff Single A real number between 0 and 1 (inclusive) usedto determine intensity map coordinates considered (with respect to theSFM algorithm) to reflect ultrasonic Waves to/from individual arrayelements. if the calculated directivity from an element to a coordinatein the intensity map is lower than directivityCutoff, then that elementis programmatically not considered to reflect sound to/from thecoordinate in question. To calculate the directivity of an element to aspecified location, a formula which approximates directivity is used(see Theory Manual [R-2]). xMin Single A real number defining the lowerlimit of x-components of intensity map coordinates (in metres). xMin isless than xMax. X-components of intensity map coordinates will rangefrom xMin to xMax. xMax Single A real number defining the upper limit ofx-components of intensity map coordinates (in metres). xMax is greaterthan xMin. xMin is less than xMax. X-components of intensity mapcoordinates will range from xMin to xMax. xSpacing Single A vector oflength n specifying x-spacings at which to iteratively performcomputation of intensities at intensity map coordinates. A total of niterations of intensity computations are performed (assuming intensitycomputations do not need to be further broken up to satisfy memoryrequirements). Coarser spacings are specified toward the lesser indicesof the vector while finer spacings are specified toward the finerindices of the vector. Additionally for every i between 1 and n- 1,dx(i + 1)/dx(i) is a positive integer. zMin Single A real numberdefining the lower limit of z-camponents of intensity map coordinates inmetres). zMin is greater or equal than zero. z-components of intensitymap coordinates will range from x to x + height, where x is the maximumof zMin and the distance corresponding to the minimum delay inlookupTable.Offset minus MinDistanceBuffer. zSpacing Single A vector oflength n specifying z-spacings at which to iteratively performcomputation of intensities at intensity map coordinates. A total of niterations of intensity computations are performed (assuming intensitycomputations do not need to be further broken up to satisfy memoryrequirements). Coarser spacings are specified toward the lesser indicesof the vector while finer spacings are specified toward the finerindices of the vector. Additionally for every i between 1 and n- 1,dz(i + 1)/dz(i) is a positive integer. height Single A real numberrepresenting a distance (in metres). minDistanceBuffer Single A realnumber representing a distance (in metres). z-components of intensitymap coordinates will range from x to x + height, where x is the maximumof zMin and the distance corresponding to the minimum delay inlookupTable.Offset minus minDistanceBuffer. zoomPercentage Single Avector of length n − 1 implicitly specifying coordinates in theintensity map to focus around upon iterative computation of coordinateintensities, For i between 1 and n − 1, zoomPercentage(i) contains areal number between 0 and 1 inclusive. On iteration i + 1 of coordinateintensity computation, where lmax is the maximum intensity calculated inthe intensity map, the coordinates in which to focus around are those ofintensity greater than zoomPercentage(i) * lmax. runNorm Boolean Thisdetermines whether or not normalization will be run on the input data.If this value is false, then the values of normThresh, normWidth andnormRunUp are unimportant as they will not be used. normThresh Single Areal number greater than 0 and less than or equal to 100. This numberspecifies the cutoff envelope height of wave packets to be normalized inA-Scan data. Where we let ‘y’ be the maximum envelope value of all FMCdata, wavepacket information with peak height greater or equal tothreshold * y/100 will be normalized. All other information in theA-Scans will be set to 0. Recall the envelope of hilbertized data issimply its magnitude. normWidth Single A positive scalar greater than 1and less than or equal to any nonzero length of AScan contained in theFMC data. This number specifies the distance (in digitization points)between distinct wave packets in an A-Scan. If the distance betweenenvelope peaks greater than the threshold variable exceeds the variablewidth, then envelope peaks are considered to belong to distinct wavepackets. Otherwise, the envelope peaks are considered to belong to thesame wave packets. normRunUp Single A positive scalar greater than 1 andless than or equal to any nonzero length of AScan contained in the FMCdata. This number specifies the number (in digitization points) of A-Scan values ahead of envelope peaks with values greater than thethreshold variable to normalize along with the envelope peaksthemselves. gratingThreshold Single A real number greater than or equal0 and less than or equal to 100. This value is used to help reduce thegrating present in the final intensity map. It works by removing lowintensity contributions from each aperture. In each aperture, thisthreshold is applied as follows. The highest intensity location in thatthreshold is the standard to which 100% is measured. Every otherlocation in the intensity map must be at least gratingThresholdpercentage of this value in order to be included in the final summation,otherwise it is zeroed out. totalMemory Single This value is the amountof memory available to this node, in bytes. It is used to fragment thecomputation of certain values, if necessary. This allows the program tofit within memory constraints. If space allows computation to occurwithout fragmenting, no fragmenting of computation occurs. tempOverrideBoolean This value determines whether or not the user input temperaturevalue will override the value stored in the slice data file. If thisvalue is false, the value for tempValue is unimportant as it will not heused. tempValue Single This is the user input value for the temperature.This value will be used for the temperature in calculations if thetempOverride value is true. Otherwise, this value will be ignored.

TABLE A3 SFM Functions Output Data/ Function Input Data/ Output DataOutput Name Input Data Class Input Data Description Class Descriptionnormalize 1. 2D 1. Filtered and 1. 2D 1. FMC data set FMClocal3matrix/single hilbertized FMC data matrix/single with normalized(complex). set (complex). peaks. All 2. scalar/single 2. cutoffthreshold used unnormalized 3. positive for normalization. data is setto integer/single 3. minimum distance zero. 4. positive (in samples)integer/single between wave packet peaks exceeding cutoff threshold suchthat the wave packets are considered distinct. 4. distance (in samples)to normalize wavepacket information before detected peaks exceedingcutoff thresholds. prepareraw 1. 2D matrix/ 1. Filtered and possibly 1.2D matrix/ 1. FMC data set dataforod single normalized (if user singleordered for (complex). has set the analysis (complex). correct 2.positive to run normalization) interpretation in integer/single FMC dataset. sfmzoom2 3. positive 2. number of probe algorithm. A-integer/single. array elements Scans belonging 4. 2D 3. size of theaperture to specific matrix/single. 4. 2D matrix whosetransmitter/receiver entries indicate the pairs are column in the FMCexpected at data set for which specific columns the A-Scan is stored, inthe FMC data for each set. transmitter/receiver pair. The transmitterand receivers correspond to the rows and columns of the matrix,respectively. pre_compute N/A N/A 1. column 1. Entry x in thearraycoords matrix/single. column matrix is the x-component position orprobe array element x. o_compute 2D matrix/double 2D matrix whoseentries 1. column 1. x-coordinate initcoords3 correspond to delay (inmatrix/single for first pass samples) between 2. column of intensitytransmission and matrix/single map recording of ultrasonic 3. columncalculation. data for matrix/single 2. z-coordinate transmitter/receiverfor pass pairs. The row and of intensity column of each entry mapcorresponds to its calculation. transmitter and receiver 3. profile pairrespectively. number for first pass of intensity map calculation. Foreach (x, z) pair, the profile number indicates which FMC data set thispair belongs to. Note: this variable array is used for parallelprocessing of OD intensity maps belonging to different data sets. Ifonly one data set is being processed at a time, every entry in theprofile array is set to 1. o_compute 1. column 1. x-coordinates of 1.column 1. x-coordinate currfinecoords4 matrix/single calculatedintensity matrix/single. of 2. column map coordinates. 2. columnneighbourhood matrix/single 2. z-coordinates of matrix/single. ofintensity 3. column calculated intensity 3. column map matrix/single mapcoordinates. matrix coordinates 4. scalar/double 3. profile numbers forsingle. exceeding calculated intensity specified map coordinates.intensity 4. loop iteration cutoff. 2. z-coordinate of neighbourhood ofintensity map coordinates exceeding specified intensity cutoff. 3.profile number of neighbourhood of intensity map coordinates exceedingspecified intensity cutoff. o_currcompute 1. column 1. probe array x- 1.2D 1. Entries of indices matrix/single coordinates matrix/single matrixyield andvisibility 2. column 2. x-coordinates used 2. 2D travel timesmatrix/single for imminent matrix/logical (in samples) 3. columnintensity map for a given matrix/single computation probe array 4.column 3. z-coordinates used element and matrix/single for imminentintensity map intensity map coordinate. computation. The row of 4.coordinate profiles the matrix used for imminent corresponds intensitymap to the (x, z) computation. coordinate index and the column of thematrix corresponds to the probe array element index. 2. Entries ofmatrix yield ‘true’ if an intensity map coordinate falls within the thespecified user defined element directivity, and ‘false’ otherwise. Therow of the matrix corresponds to the (x, z) coordinate index and thecolumn of the matrix corresponds to the probe array element index.sfmzoom2 1. 3D 1. Filtered and 1. 1D 1. Computed Matrix/singlehilbertized FMC Matrix/single intensities for (complex). data.coordinates 2. 1D column 2. Column matrix stored in indicesmatrix/single indicating profile identical to those 3. 2D number foreach row of input item 2. matrix/single index in input items 4. 2D 2 and3. matrix/logical 3. Entries of matrix 5. 3D yield travel times (inmatrix/single samples) for a given 6. scalar/single probe array elementand intensity map coordinate. The row of the matrix corresponds to the(x, z) coordinate index and the column of the matrix corresponds to theprobe array element index. 4. Entries of matrix yield ‘true’ if anintensity map coordinate falls within the the specified user definedelement directivity, and ‘false’ otherwise. The row of the matrixcorresponds to the (x, z) coordinate index and the column of the matrixcorresponds to the probe array element index. 5. Entry [i, j, k] of thematrix gives the samples between pulse transmission from element j andreceiver reception by element k 6. Grating threshold that values fromeach aperture should pass before being added to the main intensity mapo_compute 1. 1D column 1. x-coordinates used 1. 1D column 1. newlycoordinates matrix/single. in intensity map matrix/single. defined x-above 2. 1D column computation 2. 1D column coordinates. cutoffmatrix/single. 2. z-coordinates used matrix/single. 2. newly 3. 1Dcolumn in intensity map 3. 1D column defined z- matrix/single.computation. matrix/single. coordinates. 4. 1D column 3. coordinateprofiles 3. profiles for matrix/single. used in imminent given 5. 1Dcolumn intensity map newly matrix computation. deifned 4. calculatedintensities coordinates. for each coordinate/profile combination. 5.Entry i in column matrix gives the intensity cutoff for profile i suchthat new coordinates around coordinates with intensities exceeding theintensity in entry i will have corresponding intensities calculated.

TABLE A4 Boundary Recognition Parameters Variable Class DescriptionsigmaMetres Double A scalar which defines the size of the convolutionwindow to implement in Canny edge detection. thresholdParams Double Atwo element vector where thresholdParams(1) defines the low cutoffthreshold and thresholdParams(2) defines the high cutoff threshold inCanny edge detection. SEDimsZX Double A two element vector (containingonly positive integers) defining rectangular structuring elementparameters used to dilate the edge detected boundary. SEDimsZX(1)contains the rectangular structuring element height (z- direction) whileDEDimsZX(2) contains the rectangular structuring elementwidth(x-direction). maxSEDims Double A two element vector (containingonly positive integers) defining rectangular structuring elementparameters used to dilate the pixels of maximum intensity found invertical slices of the input intensity map. maxSEDims (1) contains therectangular structuring element height (z-direction) while maxSEDims (2)contains the rectangular structuring element Width(x-direction).edgeMaxDistance Double A scalar defining the maximum allowable distance(in the z-direction) between Canny edge detected points and points ofpoints of maximum intensity (in the z-direction in the intensity map.lengthTrim Double A scalar defining the length (in the x-direction) ofedge detected connected components ends to trim and replace withtranslated maximum intensity coordinates found in the same verticalslices as the trimmed edges. minAcceptableLength Double A scalardefining the minimum acceptable length in the x- direction) of thecalculated boundary. If the length of the calculated boundary is lessthan the minimum acceptable length, no boundary is returned.maxDisjointPieces Double A scalar defining the maximum acceptable numberof horizontally connected components in the calculated boundary. It thenumber of horizontally connected components exceeds maxDisjointPieces,no boundary is returned.

TABLE A5 Boundary Recognition Functions Input Data/ Output Data/Function Input Data Input Data Output Data Name Class Description ClassOutput Description makematrix 1. column Items 1-2 are (x-z) Two Matrixcontaining outofsparse matrix/ coordinates of the dimensional intensityvalues. single intensity map. Item 3 matrix/double Intensity values and2. column is the stored intensity their locations are matrix/ for each(x, z) stored in items 1-3. If single coordinate. Thus, no intensity canbe 3. column items 1-3 have equal mapped to a matrix matrix/ length.Items 4-5 are entry, NaN is stored in single the minimum that entry. 4.Scalar spacings between x variable/ and z coordinates singlerespectively. 5. Scalar variable/ single fillnan Two Matrix containingTwo Output matrix is dimensional intensity values of dimensionalidentical to input matrix matrix/double mapped sparse matrix/double withsparse NaN coordinate triples, entries filled with (x, z, i). intensityvalues of neighbouring, entry intensities. Large blocks of NaN entriesare filled with the value 0. leading_edge 1. Two Item 1 contains 1.Two 1. Binary image dimensional intensity map in 2D dimensionalcontaining detected matrix/ matrix format. matrix/logical edges of inputitem double Items 2 and 3 2. Two 1. 2. scalar contain Canny edgedimensional 2. smoothed version variable/ detection low andmatrix/double of input item 1. double high threshold values 3. scalarrespectively. variable/ Items 4 and 5 double contain Canny edge 4.scalar detection horizontal variable/ and vertical sigma double valuesrespectively. 5. scalar variable/ double computepoints 1. row Items 1and 2 1. row Items 1 and 2 contain below matrix/single contain x and zmatrix/single respectively the x and z edgewith 2. row coordinates 2.row coordinates of indistance matrix/single (respectively) ofmatrixidouble maximum intensity 3. row maximum intensity pixels lyingdirectly matrix/single pixels of vertical below (having identical 4. rowslices of smoothed x values but greater z matrix/single map output fromvalues) the edges 5. scalar leading_edge outputted from functionvariable/ function. leading_edge. double Items 3 and 4 contain x and zcoordinates (respectively) of edges output from leading_edge function.computepoints 1. row Items 1 and 2 1. row Items 1 and 2 are abovematrix/single contain respectively matrix/single respectively the x andz maxline 2. row the x and z 2. row coordinates of edges matrix/singlecoordinates of edges matrix/double outputted from function 3. row outputfrom the leading edge above matrix/single leading_edge (having identicalx 4. row matrix/ function. values but smaller z double Items 3 and 4values) input items 3 contain respectively and 4. x and z coordinates ofmaximum intensity pixels outputted from function computepointsbelowedgewithindistance. computepoints 1. row Items 1 and 2 1. row Items 1and 2 are within matrix/single contain respectively matrix/singlerespectively the x and z distance 2. row the x and z edge 2. rowcoordinates of edges matrix/single coordinates output matrix/doublewithin the vertical 3. row from function distance of maximummatrix/single computepointsabove intensity coordinates 4. row maxlineoutput from function matrix/double Items 3 and 4 computepointsbelowed 5.scalar contain respectively gewithindistance variable/ the x and zdouble coordinates the maximum intensity pixels output from functioncomputepointsbelow edgewithindistance Item 5 is the distance (in metres)specifying the maximum distance between input coordinates (1 and 2) andinput coordinates (3 and 4) coords2matrix 1. Two element Item 1 containsthe Two A binary image vector/ height (first element) dimensionalcontaining true in double and width (second matrix/logical entriesmapped from 2. Two element element) of the edge locations in inputvector/single intensity map matrix items 4 and 5. False is 3. Twoelement output from function contained in all other vector/singlemakematrixoutofsparse entries. 4. row Item 2 (resp. 3) vector/singlecontains the lower 5. row vector/ and higher x-axis double (resp.z-axis) limits of the intensity map in its first and second elements.Item 4 (resp. 5) contains the x-axis (resp. z-axis) values of the edgecoordinates output from function. imdilate 1. Two 1. binary image TwoInput two dimensional dimensional output from dimensional binary matrixpost matrix/logical function matrix/logical dilation operation. 2.rectangular coords2matrix structuring 2. rectangular element/structuring structuring element used element. in morphological dilationoperation of input item 1. bwlabeln Two Binary image Two Input matrixwith false dimensional output from dimensional entries set to 0 andmatrix/logical function imdilate matrix/double individual connectedcomponents enumerated by the positive integers 1, 2, 3, . . . bwmorph 1.Two 1. Binary image Two Input binary image dimensional output fromdimensional thinned. matrix/logical function imdilate. matrix/logical 2.row 2. string defining vector/string morphology 3. scalar operation.String variable/ is input as double constant, ‘thin’. 3. scalar definingthe number of thinning operations to perform on input binary image(input item 1). This scalar is set to Inf such that thinning operationswill continue to be successively applied until they are no longereffective. trimcompon Two Binary image output Two Connected samelinedimensional from bwmorph dimensional components of input matrix/logicalmatrix/logical binary image trimmed such that smaller connectedcomponents with pixels on the same vertical slices as larger connectedcomponents have these pixels set to false. removeall Two Binary imageoutput Two Input binary image with connections dimensional fromdimensional all junctions between between matrix/logicaltrimcomponsameline matrix/logical lines removed. lines removebottom TwoBinary image output Two Connected component dimensional from dimensionalcomponents of input matrix/logical removeallconnections matrix/logicalbinary image trimmed betweenlines such that only their top (lowestcorresponding z value) pixels with respect to any column in the matrixare preserved. removecompon Two Binary image output Two Connected samedimensional from dimensional components of input vertline matrix/logicalremovebottomcomponent matrix/logical binary image totally removed suchthat smaller connected components with pixels on the same verticalslices as larger connected components are set to false. replacecigar 1.Two 1. Binary image Two Binary image tips dimensional output fromfunction dimensional containing input item 1 matrix/logicalremovecomponsame with ends of 2. Two vertline containing matrix/logicalhorizontally connected dimensional edges output from components replacedmatrix/logical function bwlabeln by vertically translated 3. scalar/belonging to a points in input item 2. double particular label 4.rectangular 2. Binary image with structuring maximum intensityelement/structuring coordinates output element. from functioncomputepointsbelow edgewithindistance 3. Calculated number of pixels totrim off the horizontal ends of input item 1. 4. rectangular structuringelement used in morphological dilation operation of input item 2.matrix2coords 1. Two Item 1 contains a 1. column Items 1 and 2 containwithnan dimensional binary image matrix/single respectively the x-axismatrix/logical outputted from 2. column values and z-axis 2. Two elementfunction matrix/single values of the calculated vector/singlereplacecigartps edge binary image 3. Two element Item 2 (resp. 3) outputfrom function vector/single contains the lower replacecigartips andhigher x-axis (resp. z-axis) limits of the intensity map in its firstand second elements. Matrix2coords 1. Two Item 1 contains a 1. columnItems 1 and 2 contain interp dimensional binary image matrix/singlerespectively the x-axis only matrix/logical outputted from 2. columnvalues and z-axis 2. Two element function matrix/single values oflinearly vector/single replacecigartps interpolated 3. Two element Item2 (resp. 3) coordinates of empty vector/single contains the lowerregions of the and higher x-axis calculated edge binary (resp. z-axis)limits of image output from the intensity map in function its first andsecond replacecigartips elements.

TABLE A6 Boundary Definition Parameters Variable Class Description widthSingle This determines the width of the median filter used in theMedianFilter function. Width determines the number of data points usedwhen choosing a median to replace a given data point with. smoothWidthSingle This determines the width of the Savitzky- Golay filter appliedin the PerformSmoothing function. smoothOrder Single This determines theorder of the polynomial used to approximate the surface in theSavitzky-Golay filter applied in the PerformSmoothing function.

TABLE A7 Boundary Definition Functions Output Data/ Function Input Data/Input Data Output Data Output Name Input Data Class Description ClassDescription MedianFilter 1. 1D column 1. x-components of 1. 1D column 1.x- matrix/single surface to be matrix/single components 2. 1D columnfiltered 2. 1D column of filtered matrix/single 2. y/z-componentsmatrix/single surface 3. positive of surface to be 2. y/z-integer/single filtered components 3. Width of median of filtered filterto be used surface Perform 1. 1D column 1. x-components of 1. 1Dcolumn 1. x- Smoothing matrix/single surface to be matrix/singlecomponents 2. 1D column filtered 2. 1D column of smoothed matrix/single2. y/z-components matrix/single surface 3. positive of surface to be 2.y/z- integer/single filtered components 4. positive 3. Width of ofsmoothed integer/single smoothing surface window to be used 4. Order ofpolynomial used in smoothing approximation

TABLE A8 IFM OD boundary Preparation Parameters Variable ClassDescription width Single This determines the width of the median filterused in the MedianFilter function. Width determines the number of datapoints used when choosing a median to replace a given data point with.smoothWidth Single This determines the width of the Savitzky-Golayfilter applied in the PerformSmoothing function. smoothOrder Single Thisdetermines the order of the polynomial used to approximate the surfacein the Savitzky-Golay filter applied in the PerformSmoothing function.surfaceSamplingInterval Single This number is a positive integer. Thenumber 1 would indicate that every point on the given OD surface shouldbe used. The number 2 would indicate to use every other point, thenumber 3 every third, et cetera.

TABLE A9 IFM OD boundary Preparation Functions Output Data/ FunctionInput Data/ Input Data Output Data Output Name Input Data ClassDescription Class Description MedianFilter 1. 1D column 1. x-componentsof 1. 1D column 1. x- matrix/single surface to be matrix/singlecomponents 2. 1D column filtered 2. 1D column of filtered matrix/single2. y/z-components matrix/single surface 3. positive of surface to be 2.y/z- integer/single filtered components 3. Width of median of filteredfilter to be used surface Perform 1. 1D column 1. x-components of 1. 1Dcolumn 1. x- Smoothing matrix/single surface to be matrix/singlecomponents 2. 1D column filtered 2. 1D column of smoothed matrix/single2. y/z-components matrix/single surface 3. positive of surface to be 2.y/z- integer/single filtered components 4. positive 3. Width of ofsmoothed integer/single smoothing surface window to be used 4. Order ofpolynomial used in smoothing approximation

TABLE A10 ID Imaging Parameters Variable Class DescriptiondirectivityCutoff Single A real number between 0 and 1 (inclusive) usedto determine OD boundary coordinates considered (with respect to the SFMalgorithm) to refract ultrasonic waves from individual array elements.If the calculated directivity from an element to an OD boundarycoordinate is lower than directivityCutoff, then that OD boundarycoordinate is programmatically not considered to refract sound from theelement. To calculate the directivity of an element to a specifiedlocation, a formula which approximates directivity is used (see TheoryManual [R − 1]). speedOffMedium2 Single A real number defining the speedat which sound travels in the second inspection material found betweenthe OD and ID surfaces. xMin Single A real number defining the absolutelower limit of x-components of intensity map coordinates (inmillimetres). xMin is less than xMax. Where y is the minimum x-componentof the OD boundary, the actual lower limit of the x-components ofintensity map coordinates is the maximum of the following two scalars,xMin and y-horzBuffer. xMax Single A real number defining the absoluteupper limit of x-components of intensity map coordinates (inmillimetres). xMin is less than xMax. Where y′ is the maximumx-component of the OD boundary, the actual upper limit of thex-components of intensity map coordinates is the minimum of thefollowing two scalars, xMax and y′+horzBuffer. horzBuffer Single A realnumber bounding x-component coordinates of the IFM intensity map. Wherey is the minimum x-component of the OD boundary, the actual lower limitof the x-components of intensity map coordinates is the maximum of thefollowing two scalars, xMin and y−horzBuffer. Where y′ is the maximumx-component of the OD boundary, the actual upper limit of thex-components of intensity map coordinates is the minimum of thefollowing two scalars, xMax and y′+horzBuffer. xSpacing Single A vectorof length n specifying x-spacings at which to iteratively performcomputation of intensities at intensity map coordinates. A total of niterations of intensity computations are performed (assuming intensitycomputations do not need to be further broken up to satisfy memoryrequirements). Coarser spacings are specified toward the lesser indicesof the vector while finer spacings are specified toward the finerindices of the vector. Additionally for every i between 1 and n − 1,dx(i + 1)/dx(i) is a positive integer. zSpacing Single A vector oflength n specifying z-spacings at which to iteratively performcomputation of intensities at intensity map coordinates. A total of niterations of intensity computations are performed (assuming intensitycomputations do not need to be further broken up to satisfy memoryrequirements). Coarser spacings are specified toward the lesser indicesof the vector while finer spacings are specified toward the finerindices of the vector. Additionally for every i between 1 and n − 1dz(i + 1)/dz(i) is a positive integer. firstDiagIndex Single A positivescalar greater than 0 and less than lastdiagindex. This number indicatesthe first element in the probe array that should be used for the IFMintensity-map computation. lastDiagIndex single A positive scalargreater than firstdiagindex and less than the number of elements in theprobe array. This number indicates the last element in the probe arraythat should be used for the IFM intensity-map computation.zoomPercentages Single A vector of length n − 1 implicitly specifyingcoordinates in the intensity map to focus around upon iterativecomputation of coordinate intensities. For i between 1 and n − 1,zoomPercentage(i) contains a real number between 0 and 1 inclusive. Oniteration i + 1 of coordinate intensity computation, where Imax is themaximum tensity calculated in the intensity map, the coordinates inwhich to focus around are those of intensity greater thanzoomPercentage(i) * Imax. maxTransmitter single A positive scalardefining the maximum absolute difference ReceiverDifference between atransmitter and receiver used in combination for IFM intensity mapcomputation. criticalPoints Char One of the following three strings:‘mins’, ‘maxes’, or ‘minsandmaxes.’ If criticalPoints is equal to ‘mins’(resp. ‘maxes’), then only local minimums (resp. maximums) of travelpath times are considered to be true travel paths of refractedultrasonic waves. If criticalPoints is equal to ‘minsandmaxes’, thenboth local minimums and local maximums of travel path times areconsidered to be true travel paths of refracted ultrasonics waves.doNormalization logical If doNormalization is set to true, thennormalization of all A- Scans for IFM computation is executed.Otherwise, if doNormalization is set to false, no normalization iscalculated. theshold Single A real number greater than 0 and less thanor equal to 100. This number specifies the cutoff envelope height ofwave packets to be normalized in A-Scan data Where we let ‘y’ be themaximum envelope value of all FMC data, wavepacket information with peakheight greater or equal to threshold * y/100 will be normalized. Allother information in the A-Scans will be set to 0. Recall the envelopeof hilbertized data is simply its magnitude. width Single A positivescalar greater than 1 and less than or equal to any nonzero length ofAScan contained in the FMC data. This number specifies the distance (indigitization points) between distinct wave packets in an A-Scan. If thedistance between envelope peaks greater than the threshold variableexceeds the variable width, then envelope peaks are considered to belongto distinct wave packets. Othemise, the envelope peaks are considered tobelong to the same wave packets. Runup single A positive scalar greaterthan 1 and less than or equal to any nonzero length of AScan containedin the FMC data. This number specifies the number (in digitizationpoints) of A- Scan values ahead of envelope peaks with values greaterthan the threshold variable to normalize along with the envelope peaksthemselves.

TABLE A11 ID Imaging Functions Input Data/ Output Data/ Function InputData Input Data Output Name Class Description Data Class OutputDescription O_compute 1. 1D column 1. x-coordinates of 1. 2D Entry (j,k)contains ‘true’ surface matrix/single probe array matrix/logical if thedirectivity from directivityfactor 2. 1D column elements. element j tosurface matrix/single 2. x-coordinates of coordinate with index k 3. 1Dcolumn surface. is greater than or equal matrix/single 3. z-coordinatesof to the 4. scalar/single. surface. ‘surfaceDirectivityCutoff’, 4.wavelength of defined globally. It excitation pulse contains ‘false’ inwater. otherwise. O_compute 1. 1D column 1. x-coordinates of 2D Entry(j,k) contains the surface matrix/single surface. matrix/single timeneeded for a sound probearray 2. 1D column 2. z-coordinates of pulse totravel from times matrix/single surface. element k in the probe 3. 1Dcolumn 3. x-coordinates of array to surface matrix/single probe arraycoordinate with index j. 4. scalar/single. elements. 4. speed of soundin water. o_compute 1. 1D column 1. x-component of 1. 1D 1. x-componentof idcoordinates matrix/single surface column coordinates of initial 2.1D column coordinates, matrix/ intensity matrix/single 2. z-component ofsingle computation. surface 2. 1D 2. z-component of coordinates columncoordinates of initial matrix/ intensity single. computation.o_compute 1. 1D column 1. x-component of 1. 1D 1. x-coordinate ofcurrfinecoords matrix/single coordinates of column neighbourhood of 4_id2. 1D column previous loop matrix/ intensity map matrix/singleiteration's single. coordinates 3. 1D column intensity 2. 1D exceedingspecified matrix/single computation. column intensity cutoff. 4. 1Dcolumn 2. z-component of matrix/ 2. z-coordinate of matrix/singlecoordinates of single. neighbourhood of 5. positive previous loopintensity map integer/ iteration's coordinates double. intensityexceeding specified computation. intensity cutoff. 3. x-component ofsurface coordinates. 4. z-component of surface coordinates. 5. iterationof intensity computation loop. O_remove 1. 1D column 1. x-componentof 1. 1D 1. x-component of idcoords matrix/single candidate columncoordinates of outsidebounds3 2. 1D column coordinates of matrix/current loop matrix/single current loop single iteration's intensity 3.1D column iteration's 2. 1D computation within matrix/single intensitycolumn defined intensity 4. 1D column computation. matrix/ mapboundaries. matrix/single 2. z-component of single. 2. z-component ofcandidate coordinates of coordinates of current loop current loopiteration's intensity iteration's computation within intensity definedinteiisity computation. map boundaries, 3. x-component of surfacecoordinates. 4. z-component of surface coordinates. O_compoute 1. 1Dcolumn 1. x-component of 1. 1D 1. sorted x-component coordvisibilitymatrix/single coordinates of column of coordinates of from 2. 1D columncurrent loop matrix/ current loop surface matrix/single iteration'ssingle iteraton's intensity. 10 3. 1D column intensity 2. 1Dcomputation. matrix/single computation. column 2. sorted z-component4.1D column 2. z-component of matrix/ of coordinates of matrix/singlecoordinates of single. current loop current loop 3. 2D iteration'sintensity iteration's matrix/ computation. intensity logical 3. Entry(j, k) is ‘true’ if computation. there is a straight 3. x-component ofline from surface surface coordinate k to coordinates. intensity map 4.z-component of coordinate j which surface does not intersectcoordinates. the input surface (except at coordinate j). Entry (j, k) is‘false’ otherwise. O_compute 1. 1D column 1. x-component of 1. 2D 1.Entry (j, k) is the surface matrix/single coordinates of matrix/ traveltime of sound coordtimes 2. 1D column current loop single. in water frommatrix/single iteration's element k to surface 3. 1D column intensitycoordinate with matrix/single computation. index j. 4. 1D column 2.z-component of matrix/single coordinates of current loop iteration'sintensity computation. 3. x-component of surface coordinates. 4.z-component of surface coordinates. O_compute 1. 2D 1. Entry (j, k) isthe 1. Structure With respect to index i of travel matrix/single traveltime of with fields: the structure times4 2. 2D sound in water a) column(corresponding to matrix/single from element k to matrix/unsignedelement i of the linear 3. 2D surface int (16- probe array).matrix/logical coordinate with bit). a) entry j contains the 4. 2D indexj. b) column index of a coordinate matrix/logical 2. Entry (j, k) matrixsingle b) entry j contains the contains the time c) column travel time(in samples) needed for a matrix/unsigned from element i to the soundpulse to integer (8 coordinate with index j in travel from bit) fielda). element k in the d) column c) entry j contains the probe array tomatrix/unsigned number travel-time surface integer (8- solutions fromelement I coordinate with bit) to the coordinate with index j. e)scalar/ index j in field a). 3. Entry (j, k) unsigned d) contains theset of contains ‘true’ if integer (8-bit) number of solutions from thedirectivity element i to all from element j to inspection coordinates.surface e) contains the length of coordinate with field d). index k isgreater than or equal to the ‘surfaceDirectivity Cutoff’, definedglobally. It contains ‘false’ 4. Entry (j, k) is ‘true’ if there is astraight line from surface coordinate k to intensity map coordinate jwhich does not intersect the input surface (except at coordinate j).Entry (j, k) is ‘false’ otherwise. O_ifmzoom8 1. 2 × 1 element 1. Sizeof 1. column 1. computed intensities matrix/double coordinatematrix/single for coordinates with 2. 2D matrix/ matrices matchingindices as complex 2. FMC data-set computed intensities. single whereA-Scans 3. 2D are contained in matrix/single the vertical 4. 2Ddimension. matrix/single 3. Entry (i, j) 5. Structure contains the withfields: column index of a) column the A-Scan matrix/unsigned belongingto int (16-bit). transmitter i and b) column receiver j. matrix/single4. Entry (i, j) c) column contains the matrix/unsigned offset (ininteger (8 bit) samples) d) column between matrix/unsigned transmissionof integer (8-bit) ultrasonic wave e) scalar/ from element i unsignedand recording of integer (8-bit) A-Scan data from receiver j. 5. Withrespect to index i of the structure (corresponding to element i of thelinear probe array). a) entry j contains the index of a coordinate b)entry j contains the travel time (in samples) from element i to thecoordinate with index j in field a). c) entry j contains the numbertravel-time solutions from element I to the coordinate with index j infield a). d) contains the set of number of solutions from element i toall inspection coordinates. e) contains the length of field d).O_compute 1. column 1. coordinate x- 1. column 1. coordinate x-coordinates matrix/single components matrix/ components With above 2.column 2. coordinate z- single. intensity values cutoff_id matrix/singlecomponents. 2. column exceeding cutoff 3. column 3. coordinate matrix/intensity value (input matrix/single intensities single. item 4). 4.scalar/single. 4. cutoff intensity 2. coordinate z- value. componentswith intensity values exceeding cutoff intensity value (input item 4).

TABLE A12 2″ Reference Block (Serial Number 001) Dimensions MeasurementLocation converted from Rounded Step Scan Block Imperial MeasurementDelta 1 2.032 2.03 2 5.0038 5.00 3 8.00608 8.01 4 10.9982 11.00 513.9827 13.98 6 16.99006 16.99 7 23.89632 23.90 3.00 8 20.89912 20.903.00 9 17.89684 17.90 3.00 10 14.89964 14.90 3.00 11 11.89736 11.90 2.9712 8.9281 8.93

TABLE A13 2″ Reference Block (Serial Number 002) Dimensions MeasurementLocation converted from Rounded Step Scan Block Imperial MeasurementDelta 1 2.032 2.03 2 5.00888 5.01 3 8.0264 8.03 4 11.0109 11.01 513.99286 13.99 6 17.00022 17.00 7 23.89378 23.89 2.99 8 20.9042 20.903.01 9 17.8943 17.69 3.00 10 14.8971 14.90 3.00 11 11.8999 11.90 2.98 128.9154 8.92

TABLE A14 2.5″ Reference Block (Serial Number 001) DimensionsMeasurement Location converted from Rounded Step Scan Block ImperialMeasurement Delta 1 1.99898 2.00 2 4.99872 5.00 3 8.001 8.00 4 10.998211.00 5 13.9954 14.00 6 16.99768 17.00 7 24.0919 24.09 3.00 8 21.094721.10 3.01 9 18.0848 18.09 2.99 10 15.09522 15.10 3.00 11 12.09802 12.103.01 12 9.0932 9.09

TABLE A15 2.5″ Reference Block (Serial Number 002) DimensionsMeasurement Location converted from Rounded Step Scan Block ImperialMeasurement Delta 1 2.00406 2.00 2 4.99364 4.99 3 7.99592 8.00 4 10.998211.00 5 13.9954 14.00 6 16.9926 16.99 7 24.09698 24.10 3.00 8 21.1023221.10 3.01 9 18.0975 18.10 2.99 10 15.10538 15.11 3.01 11 12.09294 12.093.00 12 9.0932 9.09

The present disclosure may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects as being onlyillustrative and not restrictive. The present disclosure intends tocover and embrace all suitable changes in technology.

1. A device for performing ultrasound scanning of a conduit, comprising:a cuff adapted to fit around a circumference of the conduit; a carriermounted slidably on the cuff and adapted to traverse the circumferenceof the conduit; an ultrasound probe mounted on the carrier andpositioned to scan the circumference of the conduit as the carriertraverses the circumference of the conduit; a carrier motor mounted onthe cuff or the carrier and used to drive the movement of the carrierabout the circumference of the object; and one or more data connectionsproviding control information for the carrier motor and the ultrasoundprobe and receiving scanning data from the ultrasound probe.
 2. Thedevice of claim 1, wherein: the cuff forms a liquid-resistant sealaround the circumference of the conduit; and further comprising a liquidfeed for receiving a liquid scanning medium and filling the volumedefined between the interior of the cuff and the exterior of the conduitwith the liquid scanning medium.
 3. The device of claim 1, furthercomprising a power connection for receiving electrical power for thecarrier motor.
 4. The device of claim 1, wherein the cuff isconfigurable between an open configuration allowing it to be fittedaround the conduit and a closed configuration encircling the conduit. 5.The device of claim 1, further comprising: an adjustable reflectormounted to the carrier; and a reflector motor for controlling an angleof the adjustable reflector in a plane substantially normal to alongitudinal axis of the object; wherein: the ultrasound probe ispositioned to scan the object via reflection of ultrasound signals offof the adjustable reflector; and the one or more data connectionsprovide control information for the reflector motor.
 6. The device ofclaim 1, further comprising a power connection for receiving electricalpower for the reflector motor.
 7. The device of claim 1, wherein theconduit is a cylinder.
 8. The device of claim 1, wherein the ultrasoundprobe is an array of ultrasound transceivers.
 9. The device of claim 1,wherein the cuff comprises a knuckle which releasably secures a firsthalf of said cuff to a second half of said cuff.
 10. The device of claim1, wherein the cuff comprises a first half of said cuff detachable froma second half of said cuff.
 11. A method for performing ultrasoundscanning of a conduit, comprising: providing an ultrasound array havinga plurality of ultrasound elements arrayed substantially parallel to alongitudinal axis of the conduit; positioning the ultrasound array toproject ultrasound signals toward an external surface of the object at afirst point about the circumference of the conduit; performing afull-matrix-capture scan of the first point about the circumference ofthe conduit, comprising: transmitting an ultrasound signal from a firstultrasound element in the ultrasound array; sensing and recordingultrasound signals received by each other ultrasound element in theultrasound array; and repeating the steps of transmitting, sensing andrecording, wherein the step of transmitting is performed in turn by eachultrasound element in the ultrasound array other than the firstultrasound element; repositioning the ultrasound array at a second pointabout the circumference of the conduit; performing a full-matrix-capturescan of the second point about the circumference of the conduit; andrepeating the steps of repositioning and performing afull-matrix-capture scan. 12-13. (canceled)
 14. A method of modeling thenear and far surfaces of an object within a scanning plane passingthrough the near and far surfaces of the object, comprising: providing aset of full-matrix-capture ultrasound scanning data corresponding to ascanning area within the scanning plane, the full-matrix-captureultrasound scanning data captured using an ultrasound array transmittingand sensing ultrasound signals through a scanning medium situatedbetween the ultrasound array and the near surface of the object andperforming the steps of: transmitting an ultrasound signal from a firstultrasound element in the ultrasound array; sensing and recordingultrasound signals received by each other ultrasound element in theultrasound array; and repeating the steps of transmitting, sensing andrecording, wherein the step of transmitting is performed by eachultrasound element in the ultrasound array other than the firstultrasound element; constructing a first intensity map of the scanningarea, comprising a plurality of points within the scanning area havingassociated intensity values, by calculating travel times of ultrasoundsignals through the scanning medium based on the full-matrix-captureultrasound scanning data; filtering the first intensity map to model theboundary of the near surface within the scanning area; using the modeledboundary of the near surface as a lens in constructing a secondintensity map, comprising a plurality of points within the scanning areahaving associated intensity values, by the application of Fermat'sPrinciple, to compute ultrasound signal travel times through both thescanning medium and the object based on the full-matrix-captureultrasound scanning data; and filtering the second intensity map tomodel the boundary of the far surface within the scanning area. 15-19.(canceled)
 20. The method of claim 1, further comprising, beforeconstructing a first intensity map, filtering the full-matrix-captureultrasound scanning data to remove noise.
 21. The method of claim 20,wherein: filtering the first intensity map comprises passing theintensity map through an edge-detection filter and using the output as amodel of the boundary of the near surface within the scanning area; andfiltering the second intensity map comprises passing the intensity mapthrough an edge-detection filter and using the output as a model of theboundary of the far surface within the scanning area.
 22. The method ofclaim 21, wherein: filtering the first intensity map and filtering thesecond intensity map each further comprise dilation of the detectededges produced by the edge-detection filter. 23-27. (canceled)
 28. Adevice for performing ultrasound scanning of an object, comprising: abody adapted to fit on the object; an ultrasound probe mounted on thebody and positioned to scan the body; one or more data connectionsproviding control information for the carrier motor and the ultrasoundprobe and receiving scanning data from the ultrasound probe; anadjustable reflector mounted to the carrier; and a reflector motor forcontrolling an angle of the adjustable reflector in a planesubstantially normal to a longitudinal axis of the object; wherein: theultrasound probe is positioned to scan the object via reflection ofultrasound signals off of the adjustable reflector; and the one or moredata connections provide control information for the reflector motor.29. The device of claim 28, wherein: the body forms a liquid-resistantseal around the circumference of the object; and further comprising aliquid feed for receiving a liquid scanning medium and filling thevolume defined between the interior of the body and the exterior of theobject with the liquid scanning medium.
 30. The device of claim 28,further comprising a power connection for receiving electrical power forthe carrier motor.
 31. The device of claim 1, further comprising a powerconnection for receiving electrical power for the reflector motor. 32.The device of claim 1, wherein the ultrasound probe is an array ofultrasound transceivers.